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models/Llama-3.1-8B-quantization-baselines/config.json

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models/Llama-3.1-8B-quantization-baselines/generation_config.json

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models/Llama-3.1-8B-quantization-baselines/special_tokens_map.json

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models/Llama-3.1-8B-quantization-baselines/tokenizer_config.json

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models/Qwen/Qwen2.5-14B/.gitattributes

@@ -0,0 +1,35 @@
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models/Qwen/Qwen2.5-14B/LICENSE

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models/Qwen/Qwen2.5-14B/README.md

@@ -0,0 +1,68 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-14B/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-14B
+
+## Introduction
+
+Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+
+- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
+- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
+- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
+- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
+
+**This repo contains the base 14B Qwen2.5 model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- Number of Parameters: 14.7B
+- Number of Paramaters (Non-Embedding): 13.1B
+- Number of Layers: 48
+- Number of Attention Heads (GQA): 40 for Q and 8 for KV
+- Context Length: 131,072 tokens
+
+**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
+
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+## Requirements
+
+The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+## Evaluation & Performance
+
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+
+For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen2.5,
+    title = {Qwen2.5: A Party of Foundation Models},
+    url = {https://qwenlm.github.io/blog/qwen2.5/},
+    author = {Qwen Team},
+    month = {September},
+    year = {2024}
+}
+
+@article{qwen2,
+      title={Qwen2 Technical Report}, 
+      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
+      journal={arXiv preprint arXiv:2407.10671},
+      year={2024}
+}
+```

models/Qwen/Qwen2.5-14B/config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

models/Qwen/Qwen2.5-14B/generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.0"
+}

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models/Qwen/Qwen2.5-14B/tokenizer_config.json

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+{
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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

models/Qwen/Qwen2.5-7B/.gitattributes

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models/Qwen/Qwen2.5-7B/LICENSE

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+
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+
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models/Qwen/Qwen2.5-7B/README.md

@@ -0,0 +1,69 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen2.5-7B
+
+## Introduction
+
+Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+
+- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
+- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
+- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
+- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
+
+**This repo contains the base 7B Qwen2.5 model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- Number of Parameters: 7.61B
+- Number of Paramaters (Non-Embedding): 6.53B
+- Number of Layers: 28
+- Number of Attention Heads (GQA): 28 for Q and 4 for KV
+- Context Length: 131,072 tokens
+
+**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
+
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+## Requirements
+
+The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+## Evaluation & Performance
+
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+
+For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen2.5,
+    title = {Qwen2.5: A Party of Foundation Models},
+    url = {https://qwenlm.github.io/blog/qwen2.5/},
+    author = {Qwen Team},
+    month = {September},
+    year = {2024}
+}
+
+@article{qwen2,
+      title={Qwen2 Technical Report}, 
+      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
+      journal={arXiv preprint arXiv:2407.10671},
+      year={2024}
+}
+```

models/Qwen/Qwen2.5-7B/config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

models/Qwen/Qwen2.5-7B/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.0"
+}

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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

models/Qwen2.5-7B-quantization-baselines/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

models/Qwen2.5-7B-quantization-baselines/chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

models/Qwen2.5-7B-quantization-baselines/config.json

@@ -0,0 +1,59 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

models/Qwen2.5-7B-quantization-baselines/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.57.3"
+}

models/Qwen2.5-7B-quantization-baselines/model.safetensors.index.json

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models/Qwen2.5-7B-quantization-baselines/special_tokens_map.json

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models/Qwen2.5-7B-quantization-baselines/tokenizer_config.json

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models/meta-llama/Llama-2-7b-hf/.gitattributes

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models/meta-llama/Llama-2-7b-hf/LICENSE.txt

@@ -0,0 +1,126 @@
+LLAMA 2 COMMUNITY LICENSE AGREEMENT	
+Llama 2 Version Release Date: July 18, 2023
+
+"Agreement" means the terms and conditions for use, reproduction, distribution and 
+modification of the Llama Materials set forth herein.
+
+"Documentation" means the specifications, manuals and documentation 
+accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
+libraries/llama-downloads/.
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if 
+you are entering into this Agreement on such person or entity's behalf), of the age 
+required under applicable laws, rules or regulations to provide legal consent and that 
+has legal authority to bind your employer or such other person or entity if you are 
+entering in this Agreement on their behalf.
+
+"Llama 2" means the foundational large language models and software and 
+algorithms, including machine-learning model code, trained model weights, 
+inference-enabling code, training-enabling code, fine-tuning enabling code and other 
+elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
+libraries/llama-downloads/.
+
+"Llama Materials" means, collectively, Meta's proprietary Llama 2 and 
+Documentation (and any portion thereof) made available under this Agreement.
+
+"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you 
+are an entity, your principal place of business is in the EEA or Switzerland) and Meta 
+Platforms, Inc. (if you are located outside of the EEA or Switzerland). 
+
+By clicking "I Accept" below or by using or distributing any portion or element of the 
+Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution. 
+
+      a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
+transferable and royalty-free limited license under Meta's intellectual property or 
+other rights owned by Meta embodied in the Llama Materials to use, reproduce, 
+distribute, copy, create derivative works of, and make modifications to the Llama 
+Materials.  
+      
+      b. Redistribution and Use.  
+
+            i. If you distribute or make the Llama Materials, or any derivative works 
+thereof, available to a third party, you shall provide a copy of this Agreement to such 
+third party. 
+            ii.  If you receive Llama Materials, or any derivative works thereof, from 
+a Licensee as part of an integrated end user product, then Section 2 of this 
+Agreement will not apply to you. 
+
+            iii. You must retain in all copies of the Llama Materials that you 
+distribute the following attribution notice within a "Notice" text file distributed as a 
+part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved."
+
+            iv. Your use of the Llama Materials must comply with applicable laws 
+and regulations (including trade compliance laws and regulations) and adhere to the 
+Acceptable Use Policy for the Llama Materials (available at 
+https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into 
+this Agreement.
+
+            v. You will not use the Llama Materials or any output or results of the 
+Llama Materials to improve any other large language model (excluding Llama 2 or 
+derivative works thereof).  
+
+2. Additional Commercial Terms. If, on the Llama 2 version release date, the 
+monthly active users of the products or services made available by or for Licensee, 
+or Licensee's affiliates, is greater than 700 million monthly active users in the 
+preceding calendar month, you must request a license from Meta, which Meta may 
+grant to you in its sole discretion, and you are not authorized to exercise any of the 
+rights under this Agreement unless or until Meta otherwise expressly grants you 
+such rights.
+            
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE 
+LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE 
+PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, 
+EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY 
+WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR 
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+FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING 
+THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR 
+USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE 
+LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, 
+NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS 
+AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, 
+CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN 
+IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF 
+ANY OF THE FOREGOING.
+ 
+5. Intellectual Property.
+
+      a. No trademark licenses are granted under this Agreement, and in 
+connection with the Llama Materials, neither Meta nor Licensee may use any name 
+or mark owned by or associated with the other or any of its affiliates, except as 
+required for reasonable and customary use in describing and redistributing the 
+Llama Materials.
+
+      b. Subject to Meta's ownership of Llama Materials and derivatives made by or 
+for Meta, with respect to any derivative works and modifications of the Llama 
+Materials that are made by you, as between you and Meta, you are and will be the 
+owner of such derivative works and modifications.
+
+      c. If you institute litigation or other proceedings against Meta or any entity 
+(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama 
+Materials or Llama 2 outputs or results, or any portion of any of the foregoing, 
+constitutes infringement of intellectual property or other rights owned or licensable 
+by you, then any licenses granted to you under this Agreement shall terminate as of 
+the date such litigation or claim is filed or instituted. You will indemnify and hold 
+harmless Meta from and against any claim by any third party arising out of or related 
+to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your 
+acceptance of this Agreement or access to the Llama Materials and will continue in 
+full force and effect until terminated in accordance with the terms and conditions 
+herein. Meta may terminate this Agreement if you are in breach of any term or 
+condition of this Agreement. Upon termination of this Agreement, you shall delete 
+and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the 
+termination of this Agreement. 
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and 
+construed under the laws of the State of California without regard to choice of law 
+principles, and the UN Convention on Contracts for the International Sale of Goods 
+does not apply to this Agreement. The courts of California shall have exclusive 
+jurisdiction of any dispute arising out of this Agreement. 
+

models/meta-llama/Llama-2-7b-hf/README.md

@@ -0,0 +1,134 @@
+---
+extra_gated_heading: Access Llama 2 on Hugging Face
+extra_gated_description: >-
+  This is a form to enable access to Llama 2 on Hugging Face after you have been
+  granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
+  license terms and acceptable use policy before submitting this form. Requests
+  will be processed in 1-2 days.
+extra_gated_button_content: Submit
+extra_gated_fields:
+  I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-2
+---
+# **Llama 2**
+Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
+
+## Model Details
+*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
+
+Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
+
+**Model Developers** Meta
+
+**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
+
+**Input** Models input text only.
+
+**Output** Models generate text only.
+
+**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
+
+
+||Training Data|Params|Content Length|GQA|Tokens|LR|
+|---|---|---|---|---|---|---|
+|Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
+|Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
+|Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
+
+*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models -  70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
+
+**Model Dates** Llama 2 was trained between January 2023 and July 2023.
+
+**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
+
+**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
+
+## Intended Use
+**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
+
+To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
+
+**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
+
+## Hardware and Software
+**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
+
+**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
+
+||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
+|---|---|---|---|
+|Llama 2 7B|184320|400|31.22|
+|Llama 2 13B|368640|400|62.44|
+|Llama 2 70B|1720320|400|291.42|
+|Total|3311616||539.00|
+
+**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
+
+## Training Data
+**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
+
+**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
+
+## Evaluation Results
+
+In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
+
+|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
+|---|---|---|---|---|---|---|---|---|---|
+|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
+|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
+|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
+|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
+|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
+|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
+|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
+
+**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
+
+|||TruthfulQA|Toxigen|
+|---|---|---|---|
+|Llama 1|7B|27.42|23.00|
+|Llama 1|13B|41.74|23.08|
+|Llama 1|33B|44.19|22.57|
+|Llama 1|65B|48.71|21.77|
+|Llama 2|7B|33.29|**21.25**|
+|Llama 2|13B|41.86|26.10|
+|Llama 2|70B|**50.18**|24.60|
+
+**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
+
+
+|||TruthfulQA|Toxigen|
+|---|---|---|---|
+|Llama-2-Chat|7B|57.04|**0.00**|
+|Llama-2-Chat|13B|62.18|**0.00**|
+|Llama-2-Chat|70B|**64.14**|0.01|
+
+**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
+
+## Ethical Considerations and Limitations
+Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
+
+Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
+
+## Reporting Issues
+Please report any software “bug,” or other problems with the models through one of the following means:
+- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+
+## Llama Model Index
+|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
+|---|---|---|---|---|
+|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
+|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
+|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

models/meta-llama/Llama-2-7b-hf/USE_POLICY.md

@@ -0,0 +1,50 @@
+# Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
+
+## Prohibited Uses
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
+
+1. Violate the law or others’ rights, including to:
+    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
+        1. Violence or terrorism 
+        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+        3. Human trafficking, exploitation, and sexual violence
+        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+        5. Sexual solicitation
+        6. Any other criminal activity
+    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
+    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
+
+
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+    2. Guns and illegal weapons (including weapon development)
+    3. Illegal drugs and regulated/controlled substances
+    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+    3. Generating, promoting, or further distributing spam
+    4. Impersonating another individual without consent, authorization, or legal right
+    5. Representing that the use of Llama 2 or outputs are human-generated
+    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+4. Fail to appropriately disclose to end users any known dangers of your AI system 
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
+

models/meta-llama/Llama-2-7b-hf/config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

models/meta-llama/Llama-2-7b-hf/generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 32000,
+  "do_sample": true,
+  "temperature": 0.9,
+  "top_p": 0.6,
+  "transformers_version": "4.31.0.dev0"
+}

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models/meta-llama/Llama-2-7b-hf/special_tokens_map.json

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+{
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+    "normalized": true,
+    "rstrip": false,
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+  "unk_token": {
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/meta-llama/Llama-3.1-8B/LICENSE

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+LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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models/meta-llama/Llama-3.1-8B/README.md

@@ -0,0 +1,1240 @@
+---
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+license: llama3.1
+extra_gated_prompt: >-
+  ### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+
+  Llama 3.1 Version Release Date: July 23, 2024
+
+  "Agreement" means the terms and conditions for use, reproduction, distribution
+  and modification of the  Llama Materials set forth herein.
+
+  "Documentation" means the specifications, manuals and documentation
+  accompanying Llama 3.1 distributed by Meta at
+  https://llama.meta.com/doc/overview.
+
+  "Licensee" or "you" means you, or your employer or any other person or entity
+  (if you are entering into this Agreement on such person or entity’s behalf),
+  of the age required under applicable laws, rules or regulations to provide
+  legal consent and that has legal authority to bind your employer or such other
+  person or entity if you are entering in this Agreement on their behalf.
+
+  "Llama 3.1" means the foundational large language models and software and
+  algorithms, including machine-learning model code, trained model weights,
+  inference-enabling code, training-enabling code, fine-tuning enabling code and
+  other elements of the foregoing distributed by Meta at
+  https://llama.meta.com/llama-downloads.
+
+  "Llama Materials" means, collectively, Meta’s proprietary Llama 3.1 and
+  Documentation (and any portion thereof) made available under this Agreement.
+
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+     
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+
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+
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+
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+
+  a. No trademark licenses are granted under this Agreement, and in connection
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+  Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a
+  license to use “Llama” (the “Mark”) solely as required to comply with the last
+  sentence of Section 1.b.i. You will comply with Meta’s brand guidelines
+  (currently accessible at
+  https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill
+  arising out of your use of the Mark will inure to the benefit of Meta.
+
+  b. Subject to Meta’s ownership of Llama Materials and derivatives made by or
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+  Materials that are made by you, as between you and Meta, you are and will be
+  the owner of such derivative works and modifications.
+
+  c. If you institute litigation or other proceedings against Meta or any entity
+  (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama
+  Materials or Llama 3.1 outputs or results, or any portion of any of the
+  foregoing, constitutes infringement of intellectual property or other rights
+  owned or licensable by you, then any licenses granted to you under this
+  Agreement shall terminate as of the date such litigation or claim is filed or
+  instituted. You will indemnify and hold harmless Meta from and against any
+  claim by any third party arising out of or related to your use or distribution
+  of the Llama Materials.
+
+  6. Term and Termination. The term of this Agreement will commence upon your
+  acceptance of this Agreement or access to the Llama Materials and will
+  continue in full force and effect until terminated in accordance with the
+  terms and conditions herein. Meta may terminate this Agreement if you are in
+  breach of any term or condition of this Agreement. Upon termination of this
+  Agreement, you shall delete and cease use of the Llama Materials. Sections 3,
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+
+  7. Governing Law and Jurisdiction. This Agreement will be governed and
+  construed under the laws of the State of California without regard to choice
+  of law principles, and the UN Convention on Contracts for the International
+  Sale of Goods does not apply to this Agreement. The courts of California shall
+  have exclusive jurisdiction of any dispute arising out of this Agreement.
+
+  ### Llama 3.1 Acceptable Use Policy
+
+  Meta is committed to promoting safe and fair use of its tools and features,
+  including Llama 3.1. If you access or use Llama 3.1, you agree to this
+  Acceptable Use Policy (“Policy”). The most recent copy of this policy can be
+  found at
+  [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
+
+  #### Prohibited Uses
+
+  We want everyone to use Llama 3.1 safely and responsibly. You agree you will
+  not use, or allow others to use, Llama 3.1 to:
+   1. Violate the law or others’ rights, including to:
+      1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
+          1. Violence or terrorism
+          2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+          3. Human trafficking, exploitation, and sexual violence
+          4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+          5. Sexual solicitation
+          6. Any other criminal activity
+      3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+      4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+      5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+      6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+      7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
+      8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
+  2. Engage in, promote, incite, facilitate, or assist in the planning or
+  development of activities that present a risk of death or bodily harm to
+  individuals, including use of Llama 3.1 related to the following:
+      1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+      2. Guns and illegal weapons (including weapon development)
+      3. Illegal drugs and regulated/controlled substances
+      4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+      5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+      6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+  3. Intentionally deceive or mislead others, including use of Llama 3.1 related
+  to the following:
+      1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+      2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+      3. Generating, promoting, or further distributing spam
+      4. Impersonating another individual without consent, authorization, or legal right
+      5. Representing that the use of Llama 3.1 or outputs are human-generated
+      6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
+  4. Fail to appropriately disclose to end users any known dangers of your AI
+  system
+
+  Please report any violation of this Policy, software “bug,” or other problems
+  that could lead to a violation of this Policy through one of the following
+  means:
+      * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
+      * Reporting risky content generated by the model:
+      developers.facebook.com/llama_output_feedback
+      * Reporting bugs and security concerns: facebook.com/whitehat/info
+      * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  Job title:
+    type: select
+    options:
+    - Student
+    - Research Graduate
+    - AI researcher
+    - AI developer/engineer
+    - Reporter
+    - Other
+  geo: ip_location
+  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
+extra_gated_description: >-
+  The information you provide will be collected, stored, processed and shared in
+  accordance with the [Meta Privacy
+  Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
+library_name: transformers
+---
+
+## Model Information
+
+The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
+
+**Model developer**: Meta
+
+**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. 
+
+
+<table>
+  <tr>
+   <td>
+   </td>
+   <td><strong>Training Data</strong>
+   </td>
+   <td><strong>Params</strong>
+   </td>
+   <td><strong>Input modalities</strong>
+   </td>
+   <td><strong>Output modalities</strong>
+   </td>
+   <td><strong>Context length</strong>
+   </td>
+   <td><strong>GQA</strong>
+   </td>
+   <td><strong>Token count</strong>
+   </td>
+   <td><strong>Knowledge cutoff</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="3" >Llama 3.1 (text only)
+   </td>
+   <td rowspan="3" >A new mix of publicly available online data.
+   </td>
+   <td>8B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+   <td rowspan="3" >15T+
+   </td>
+   <td rowspan="3" >December 2023
+   </td>
+  </tr>
+  <tr>
+   <td>70B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+  <tr>
+   <td>405B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+</table>
+
+
+**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
+
+**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
+
+**Model Release Date:** July 23, 2024.
+
+**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
+
+**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+
+Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 
+
+
+## Intended Use
+
+**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. 
+
+**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
+
+**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
+
+## How to use
+
+This repository contains two versions of Meta's Llama-3.1-8B, for use with transformers and with the original `llama` codebase.
+
+### Use with transformers
+
+Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+```python
+import transformers
+import torch
+
+model_id = "meta-llama/Llama-3.1-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
+)
+
+pipeline("Hey how are you doing today?")
+```
+
+### Use with `llama`
+
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
+
+To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
+
+```
+huggingface-cli download meta-llama/Llama-3.1-8B --include "original/*" --local-dir Llama-3.1-8B
+```
+
+## Hardware and Software
+
+**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
+
+**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. 
+
+
+**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
+
+
+<table>
+  <tr>
+   <td>
+   </td>
+   <td><strong>Training Time (GPU hours)</strong>
+   </td>
+   <td><strong>Training Power Consumption (W)</strong>
+   </td>
+   <td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
+<p>
+<strong>(tons CO2eq)</strong>
+   </td>
+   <td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
+<p>
+<strong>(tons CO2eq)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 8B
+   </td>
+   <td>1.46M
+   </td>
+   <td>700
+   </td>
+   <td>420
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 70B
+   </td>
+   <td>7.0M
+   </td>
+   <td>700
+   </td>
+   <td>2,040
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 405B
+   </td>
+   <td>30.84M
+   </td>
+   <td>700
+   </td>
+   <td>8,930
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Total
+   </td>
+   <td>39.3M
+   <td>
+<ul>
+
+</ul>
+   </td>
+   <td>11,390
+   </td>
+   <td>0
+   </td>
+  </tr>
+</table>
+
+
+
+The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149).  Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions  will not be incurred by others.
+
+
+## Training Data
+
+**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. 
+
+**Data Freshness:** The pretraining data has a cutoff of December 2023.
+
+
+## Benchmark scores
+
+In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. 
+
+### Base pretrained models
+
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong># Shots</strong>
+   </td>
+   <td><strong>Metric</strong>
+   </td>
+   <td><strong>Llama 3 8B</strong>
+   </td>
+   <td><strong>Llama 3.1 8B</strong>
+   </td>
+   <td><strong>Llama 3 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 405B</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" >General
+   </td>
+   <td>MMLU
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc_char
+   </td>
+   <td>66.7
+   </td>
+   <td>66.7
+   </td>
+   <td>79.5
+   </td>
+   <td>79.3
+   </td>
+   <td>85.2
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-Pro (CoT)
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc_char
+   </td>
+   <td>36.2
+   </td>
+   <td>37.1
+   </td>
+   <td>55.0
+   </td>
+   <td>53.8
+   </td>
+   <td>61.6
+   </td>
+  </tr>
+  <tr>
+   <td>AGIEval English
+   </td>
+   <td>3-5
+   </td>
+   <td>average/acc_char
+   </td>
+   <td>47.1
+   </td>
+   <td>47.8
+   </td>
+   <td>63.0
+   </td>
+   <td>64.6
+   </td>
+   <td>71.6
+   </td>
+  </tr>
+  <tr>
+   <td>CommonSenseQA
+   </td>
+   <td>7
+   </td>
+   <td>acc_char
+   </td>
+   <td>72.6
+   </td>
+   <td>75.0
+   </td>
+   <td>83.8
+   </td>
+   <td>84.1
+   </td>
+   <td>85.8
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande
+   </td>
+   <td>5
+   </td>
+   <td>acc_char
+   </td>
+   <td>-
+   </td>
+   <td>60.5
+   </td>
+   <td>-
+   </td>
+   <td>83.3
+   </td>
+   <td>86.7
+   </td>
+  </tr>
+  <tr>
+   <td>BIG-Bench Hard (CoT)
+   </td>
+   <td>3
+   </td>
+   <td>average/em
+   </td>
+   <td>61.1
+   </td>
+   <td>64.2
+   </td>
+   <td>81.3
+   </td>
+   <td>81.6
+   </td>
+   <td>85.9
+   </td>
+  </tr>
+  <tr>
+   <td>ARC-Challenge
+   </td>
+   <td>25
+   </td>
+   <td>acc_char
+   </td>
+   <td>79.4
+   </td>
+   <td>79.7
+   </td>
+   <td>93.1
+   </td>
+   <td>92.9
+   </td>
+   <td>96.1
+   </td>
+  </tr>
+  <tr>
+   <td>Knowledge reasoning
+   </td>
+   <td>TriviaQA-Wiki
+   </td>
+   <td>5
+   </td>
+   <td>em
+   </td>
+   <td>78.5
+   </td>
+   <td>77.6
+   </td>
+   <td>89.7
+   </td>
+   <td>89.8
+   </td>
+   <td>91.8
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Reading comprehension
+   </td>
+   <td>SQuAD
+   </td>
+   <td>1
+   </td>
+   <td>em
+   </td>
+   <td>76.4
+   </td>
+   <td>77.0
+   </td>
+   <td>85.6
+   </td>
+   <td>81.8
+   </td>
+   <td>89.3
+   </td>
+  </tr>
+  <tr>
+   <td>QuAC (F1)
+   </td>
+   <td>1
+   </td>
+   <td>f1
+   </td>
+   <td>44.4
+   </td>
+   <td>44.9
+   </td>
+   <td>51.1
+   </td>
+   <td>51.1
+   </td>
+   <td>53.6
+   </td>
+  </tr>
+  <tr>
+   <td>BoolQ
+   </td>
+   <td>0
+   </td>
+   <td>acc_char
+   </td>
+   <td>75.7
+   </td>
+   <td>75.0
+   </td>
+   <td>79.0
+   </td>
+   <td>79.4
+   </td>
+   <td>80.0
+   </td>
+  </tr>
+  <tr>
+   <td>DROP (F1)
+   </td>
+   <td>3
+   </td>
+   <td>f1
+   </td>
+   <td>58.4
+   </td>
+   <td>59.5
+   </td>
+   <td>79.7
+   </td>
+   <td>79.6
+   </td>
+   <td>84.8
+   </td>
+  </tr>
+</table>
+
+
+
+### Instruction tuned models
+
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong># Shots</strong>
+   </td>
+   <td><strong>Metric</strong>
+   </td>
+   <td><strong>Llama 3 8B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 8B Instruct</strong>
+   </td>
+   <td><strong>Llama 3 70B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 70B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 405B Instruct</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >General
+   </td>
+   <td>MMLU
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>68.5
+   </td>
+   <td>69.4
+   </td>
+   <td>82.0
+   </td>
+   <td>83.6
+   </td>
+   <td>87.3
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>65.3
+   </td>
+   <td>73.0
+   </td>
+   <td>80.9
+   </td>
+   <td>86.0
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-Pro (CoT)
+   </td>
+   <td>5
+   </td>
+   <td>micro_avg/acc_char
+   </td>
+   <td>45.5
+   </td>
+   <td>48.3
+   </td>
+   <td>63.4
+   </td>
+   <td>66.4
+   </td>
+   <td>73.3
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval
+   </td>
+   <td>
+   </td>
+   <td>
+   </td>
+   <td>76.8
+   </td>
+   <td>80.4
+   </td>
+   <td>82.9
+   </td>
+   <td>87.5
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" >Reasoning
+   </td>
+   <td>ARC-C
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>82.4
+   </td>
+   <td>83.4
+   </td>
+   <td>94.4
+   </td>
+   <td>94.8
+   </td>
+   <td>96.9
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA
+   </td>
+   <td>0
+   </td>
+   <td>em
+   </td>
+   <td>34.6
+   </td>
+   <td>30.4
+   </td>
+   <td>39.5
+   </td>
+   <td>46.7
+   </td>
+   <td>50.7
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Code
+   </td>
+   <td>HumanEval
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>60.4
+   </td>
+   <td>72.6
+   </td>
+   <td>81.7
+   </td>
+   <td>80.5
+   </td>
+   <td>89.0
+   </td>
+  </tr>
+  <tr>
+   <td>MBPP ++ base version
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>70.6
+   </td>
+   <td>72.8
+   </td>
+   <td>82.5
+   </td>
+   <td>86.0
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td>Multipl-E HumanEval
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>-
+   </td>
+   <td>50.8
+   </td>
+   <td>-
+   </td>
+   <td>65.5
+   </td>
+   <td>75.2
+   </td>
+  </tr>
+  <tr>
+   <td>Multipl-E MBPP
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>-
+   </td>
+   <td>52.4
+   </td>
+   <td>-
+   </td>
+   <td>62.0
+   </td>
+   <td>65.7
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" >Math
+   </td>
+   <td>GSM-8K (CoT)
+   </td>
+   <td>8
+   </td>
+   <td>em_maj1@1
+   </td>
+   <td>80.6
+   </td>
+   <td>84.5
+   </td>
+   <td>93.0
+   </td>
+   <td>95.1
+   </td>
+   <td>96.8
+   </td>
+  </tr>
+  <tr>
+   <td>MATH (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>final_em
+   </td>
+   <td>29.1
+   </td>
+   <td>51.9
+   </td>
+   <td>51.0
+   </td>
+   <td>68.0
+   </td>
+   <td>73.8
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Tool Use
+   </td>
+   <td>API-Bank
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>48.3
+   </td>
+   <td>82.6
+   </td>
+   <td>85.1
+   </td>
+   <td>90.0
+   </td>
+   <td>92.0
+   </td>
+  </tr>
+  <tr>
+   <td>BFCL
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>60.3
+   </td>
+   <td>76.1
+   </td>
+   <td>83.0
+   </td>
+   <td>84.8
+   </td>
+   <td>88.5
+   </td>
+  </tr>
+  <tr>
+   <td>Gorilla Benchmark API Bench
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>1.7
+   </td>
+   <td>8.2
+   </td>
+   <td>14.7
+   </td>
+   <td>29.7
+   </td>
+   <td>35.3
+   </td>
+  </tr>
+  <tr>
+   <td>Nexus (0-shot)
+   </td>
+   <td>0
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>18.1
+   </td>
+   <td>38.5
+   </td>
+   <td>47.8
+   </td>
+   <td>56.7
+   </td>
+   <td>58.7
+   </td>
+  </tr>
+  <tr>
+   <td>Multilingual
+   </td>
+   <td>Multilingual MGSM (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>em
+   </td>
+   <td>-
+   </td>
+   <td>68.9
+   </td>
+   <td>-
+   </td>
+   <td>86.9
+   </td>
+   <td>91.6
+   </td>
+  </tr>
+</table>
+
+#### Multilingual benchmarks
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Language</strong>
+   </td>
+   <td><strong>Llama 3.1 8B</strong>
+   </td>
+   <td><strong>Llama 3.1 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 405B</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="9" ><strong>General</strong>
+   </td>
+   <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
+   </td>
+   <td>Portuguese
+   </td>
+   <td>62.12
+   </td>
+   <td>80.13
+   </td>
+   <td>84.95
+   </td>
+  </tr>
+  <tr>
+   <td>Spanish
+   </td>
+   <td>62.45
+   </td>
+   <td>80.05
+   </td>
+   <td>85.08
+   </td>
+  </tr>
+  <tr>
+   <td>Italian
+   </td>
+   <td>61.63
+   </td>
+   <td>80.4
+   </td>
+   <td>85.04
+   </td>
+  </tr>
+  <tr>
+   <td>German
+   </td>
+   <td>60.59
+   </td>
+   <td>79.27
+   </td>
+   <td>84.36
+   </td>
+  </tr>
+  <tr>
+   <td>French
+   </td>
+   <td>62.34
+   </td>
+   <td>79.82
+   </td>
+   <td>84.66
+   </td>
+  </tr>
+  <tr>
+   <td>Hindi
+   </td>
+   <td>50.88
+   </td>
+   <td>74.52
+   </td>
+   <td>80.31
+   </td>
+  </tr>
+  <tr>
+   <td>Thai
+   </td>
+   <td>50.32
+   </td>
+   <td>72.95
+   </td>
+   <td>78.21
+   </td>
+  </tr>
+</table>
+
+
+
+## Responsibility & Safety
+
+As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
+
+
+
+* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. 
+* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
+* Provide protections for the community to help prevent the misuse of our models.
+
+
+### Responsible deployment 
+
+Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. 
+
+
+#### Llama 3.1 instruct 
+
+Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. 
+
+**Fine-tuning data**
+
+We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. 
+
+**Refusals and Tone**
+
+Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow  tone guidelines. 
+
+
+#### Llama 3.1 systems
+
+**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. 
+
+As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. 
+
+
+#### New capabilities 
+
+Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
+
+**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. 
+
+**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. 
+
+
+### Evaluations
+
+We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. 
+
+Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
+
+**Red teaming**
+
+For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. 
+
+We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity.  The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
+
+
+### Critical and other risks 
+
+We specifically focused our efforts on mitigating the following critical risk areas:
+
+**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
+
+To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 
+
+
+**2. Child Safety**
+
+Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development.  For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 
+
+**3. Cyber attack enablement**
+
+Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
+
+Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
+
+Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
+
+
+### Community 
+
+Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). 
+
+We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). 
+
+Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
+
+
+## Ethical Considerations and Limitations
+
+The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. 
+
+But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.

models/meta-llama/Llama-3.1-8B/config.json

@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "low_freq_factor": 1.0,
+    "high_freq_factor": 4.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

models/meta-llama/Llama-3.1-8B/original/params.json

@@ -0,0 +1 @@
+{"dim": 4096, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": true, "vocab_size": 128256}

models/meta-llama/Llama-3.1-8B/special_tokens_map.json

@@ -0,0 +1,4 @@
+{
+  "bos_token": "<|begin_of_text|>",
+  "eos_token": "<|end_of_text|>"
+}

models/meta-llama/Llama-3.1-8B/tokenizer_config.json

@@ -0,0 +1,2061 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
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