Text Generation
Transformers
Safetensors
GGUF
English
llama
dnd
dungeons-and-dragons
rpg
qlora
tinyllama
conversational
text-generation-inference
Instructions to use JBHarris/dm-llm-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JBHarris/dm-llm-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JBHarris/dm-llm-tiny") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JBHarris/dm-llm-tiny") model = AutoModelForCausalLM.from_pretrained("JBHarris/dm-llm-tiny") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use JBHarris/dm-llm-tiny with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="JBHarris/dm-llm-tiny", filename="dm-llm-tiny-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use JBHarris/dm-llm-tiny with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JBHarris/dm-llm-tiny:Q4_K_M # Run inference directly in the terminal: llama-cli -hf JBHarris/dm-llm-tiny:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JBHarris/dm-llm-tiny:Q4_K_M # Run inference directly in the terminal: llama-cli -hf JBHarris/dm-llm-tiny:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf JBHarris/dm-llm-tiny:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf JBHarris/dm-llm-tiny:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf JBHarris/dm-llm-tiny:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf JBHarris/dm-llm-tiny:Q4_K_M
Use Docker
docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use JBHarris/dm-llm-tiny with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JBHarris/dm-llm-tiny" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JBHarris/dm-llm-tiny", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
- SGLang
How to use JBHarris/dm-llm-tiny with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JBHarris/dm-llm-tiny" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JBHarris/dm-llm-tiny", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JBHarris/dm-llm-tiny" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JBHarris/dm-llm-tiny", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use JBHarris/dm-llm-tiny with Ollama:
ollama run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
- Unsloth Studio new
How to use JBHarris/dm-llm-tiny with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for JBHarris/dm-llm-tiny to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for JBHarris/dm-llm-tiny to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JBHarris/dm-llm-tiny to start chatting
- Docker Model Runner
How to use JBHarris/dm-llm-tiny with Docker Model Runner:
docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
- Lemonade
How to use JBHarris/dm-llm-tiny with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JBHarris/dm-llm-tiny:Q4_K_M
Run and chat with the model
lemonade run user.dm-llm-tiny-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- README.md +65 -0
- chat_template.jinja +15 -0
- config.json +29 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
README.md
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| 1 |
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- dnd
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- dungeons-and-dragons
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- rpg
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- text-generation
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- qlora
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- tinyllama
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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pipeline_tag: text-generation
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---
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# DM-LLM-Tiny
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A tiny (1.1B parameter) language model fine-tuned for **Dungeons & Dragons** content generation.
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## What it does
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Generates creative D&D content including:
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- **NPCs** — memorable characters with backstories, motivations, and quirks
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- **Quests** — hooks, outlines, and full quest arcs
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- **Dialog** — in-character conversations, monologues, and banter
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- **Locations** — vivid descriptions of dungeons, towns, and wilderness
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- **Encounters** — combat, social, and puzzle encounters
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## Usage
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### With Ollama (easiest)
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```bash
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ollama run JBHarris/dm-llm-tiny
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```
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### With Transformers
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="JBHarris/dm-llm-tiny")
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messages = [
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{"role": "system", "content": "You are a creative D&D dungeon master's assistant."},
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{"role": "user", "content": "Create a mysterious NPC for a tavern scene."},
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]
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result = pipe(messages, max_new_tokens=512)
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print(result[0]["generated_text"][-1]["content"])
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```
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## Training
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- **Base model:** TinyLlama-1.1B-Chat-v1.0
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- **Method:** QLoRA (4-bit NF4 quantization + LoRA r=64)
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- **Data:** ~500 synthetic D&D instruction/response pairs generated with Claude
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- **Hardware:** NVIDIA RTX 4080 16GB
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## Limitations
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This is a 1.1B parameter model. It's creative and fun for brainstorming but will not match
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the quality of larger models (7B+). Best used as a quick idea generator, not a replacement
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for a human DM's judgment.
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## License
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Apache 2.0 (same as base model)
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chat_template.jinja
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{% for message in messages %}
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{% if message['role'] == 'user' %}
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{{ '<|user|>
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' + message['content'] + eos_token }}
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{% elif message['role'] == 'system' %}
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{{ '<|system|>
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' + message['content'] + eos_token }}
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{% elif message['role'] == 'assistant' %}
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{{ '<|assistant|>
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' + message['content'] + eos_token }}
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{% endif %}
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{% if loop.last and add_generation_prompt %}
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{{ '<|assistant|>' }}
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{% endif %}
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{% endfor %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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| 7 |
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"bos_token_id": 1,
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"dtype": "float16",
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| 9 |
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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| 14 |
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"intermediate_size": 5632,
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| 15 |
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"max_position_embeddings": 2048,
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| 16 |
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.6",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 2048,
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"pad_token_id": 0,
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"transformers_version": "4.57.6"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:65d8b73a5513e8baaba6654eabdb4365a9ddde0db42797ec78fbdbab97249c4a
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size 2200119664
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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| 7 |
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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| 19 |
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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| 23 |
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"unk_token": {
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| 24 |
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"content": "<unk>",
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| 25 |
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"lstrip": false,
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| 26 |
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"normalized": false,
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| 27 |
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"rstrip": false,
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"single_word": false
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}
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| 30 |
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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| 3 |
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size 499723
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tokenizer_config.json
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{
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| 2 |
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"add_bos_token": true,
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| 3 |
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"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": null,
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| 5 |
+
"added_tokens_decoder": {
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| 6 |
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"0": {
|
| 7 |
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"content": "<unk>",
|
| 8 |
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"lstrip": false,
|
| 9 |
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"normalized": false,
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| 10 |
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"rstrip": false,
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| 11 |
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"single_word": false,
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| 12 |
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"special": true
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| 13 |
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},
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| 14 |
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"1": {
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| 15 |
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"content": "<s>",
|
| 16 |
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"lstrip": false,
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| 17 |
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"normalized": false,
|
| 18 |
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"rstrip": false,
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| 19 |
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"single_word": false,
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| 20 |
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"special": true
|
| 21 |
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},
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| 22 |
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"2": {
|
| 23 |
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"content": "</s>",
|
| 24 |
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"lstrip": false,
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| 25 |
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"normalized": false,
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| 26 |
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"rstrip": false,
|
| 27 |
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"single_word": false,
|
| 28 |
+
"special": true
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
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"bos_token": "<s>",
|
| 32 |
+
"clean_up_tokenization_spaces": false,
|
| 33 |
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"eos_token": "</s>",
|
| 34 |
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"extra_special_tokens": {},
|
| 35 |
+
"legacy": false,
|
| 36 |
+
"model_max_length": 2048,
|
| 37 |
+
"pad_token": "</s>",
|
| 38 |
+
"padding_side": "right",
|
| 39 |
+
"sp_model_kwargs": {},
|
| 40 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 41 |
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"unk_token": "<unk>",
|
| 42 |
+
"use_default_system_prompt": false
|
| 43 |
+
}
|