| | --- |
| | license: cc-by-nc-4.0 |
| | base_model: google/gemma-2b-it |
| | tags: |
| | - generated_from_trainer |
| | - axolotl |
| | - gemma |
| | - instruct |
| | - finetune |
| | - chatml |
| | - gpt4 |
| | - synthetic data |
| | - distillation |
| | model-index: |
| | - name: gemma-2b-openhermes |
| | results: [] |
| | datasets: |
| | - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha |
| | language: |
| | - en |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | --- |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # gemma-2b-openhermes |
| |
|
| |
|
| |  |
| |
|
| | gemma-2b-openhermes is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset |
| | using QLoRA. |
| |
|
| |
|
| | * [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) |
| | * [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha) |
| |
|
| | </details><br> |
| |
|
| | ## Usage |
| |
|
| | ### Chat Template |
| |
|
| | The instruction-tuned models use a chat template that must be adhered to for conversational use. |
| | The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. |
| |
|
| | Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: |
| |
|
| | ```py |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | import transformers |
| | import torch |
| | |
| | model_id = "abideen/gemma-2b-openhermes" |
| | dtype = torch.bfloat16 |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_id, |
| | device_map="cuda", |
| | torch_dtype=dtype, |
| | ) |
| | |
| | chat = [{ "role": "user", "content": "What is a Language Model?" }] |
| | prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
| | ``` |
| |
|
| | After the prompt is ready, generation can be performed like this: |
| |
|
| | ```py |
| | inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") |
| | outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=250) |
| | print(tokenizer.decode(outputs[0])) |
| | ``` |
| |
|
| | ### Inputs and outputs |
| |
|
| | * **Input:** Text string, such as a question, a prompt, or a document to be |
| | summarized. |
| | * **Output:** Generated English-language text in response to the input, such |
| | as an answer to a question, or a summary of a document. |
| | |
| | ## 🏆 Evaluation results |
| |
|
| | # Nous Benchmark |
| |
|
| | Agieval |
| |
|
| | | Task | Version | Metric | Value | | StdErr | |
| | |-------------------------------------------|---------|--------|-------|---|---------| |
| | | agieval\_aqua\_rat | 0 | acc | 24.02 | _ | 2.69 | |
| | | agieval\_aqua\_rat | 0 | acc\_norm | 24.02 | _ | 2.69 | |
| | | agieval\_logiqa\_en | 0 | acc | 23.20 | _ | 1.66 | |
| | | agieval\_logiqa\_en | 0 | acc\_norm | 24.42 | _ | 1.69 | |
| | | agieval\_lsat\_ar | 0 | acc | 18.26 | _ | 2.55 | |
| | | agieval\_lsat\_ar | 0 | acc\_norm | 18.70 | _ | 2.58 | |
| | | agieval\_lsat\_lr | 0 | acc | 22.35 | _ | 1.85 | |
| | | agieval\_lsat\_lr | 0 | acc\_norm | 23.53 | _ | 1.88 | |
| | | agieval\_lsat\_rc | 0 | acc | 20.82 | _ | 2.48 | |
| | | agieval\_lsat\_rc | 0 | acc\_norm | 20.07 | _ | 2.45 | |
| | | agieval\_sat\_en | 0 | acc | 32.52 | _ | 3.27 | |
| | | agieval\_sat\_en | 0 | acc\_norm | 32.52 | _ | 3.27 | |
| | | agieval\_sat\_en\_without\_passage | 0 | acc | 25.73 | _ | 3.05 | |
| | | agieval\_sat\_en\_without\_passage | 0 | acc\_norm | 24.27 | _ | 2.99 | |
| | | agieval\_sat\_math | 0 | acc | 25.00 | _ | 2.93 | |
| | | agieval\_sat\_math | 0 | acc\_norm | 20.91 | _ | 2.75 | |
| | Average: 24.11 |
| |
|
| | GPT4ALL |
| |
|
| | | Task | Version | Metric | Value | | StdErr | |
| | |----------------------|---------|--------|-------|---|---------| |
| | | arc\_challenge | 0 | acc | 21.77 | _ | 1.21 | |
| | | arc\_challenge | 0 | acc\_norm | 24.15 | _ | 1.25 | |
| | | arc\_easy | 0 | acc | 37.37 | _ | 0.99 | |
| | | arc\_easy | 0 | acc\_norm | 36.95 | _ | 0.99 | |
| | | boolq | 1 | acc | 65.60 | _ | 0.83 | |
| | | hellaswag | 0 | acc | 34.54 | _ | 0.47 | |
| | | hellaswag | 0 | acc\_norm | 40.54 | _ | 0.49 | |
| | | openbookqa | 0 | acc | 15.00 | _ | 1.59 | |
| | | openbookqa | 0 | acc\_norm | 27.40 | _ | 2.00 | |
| | | piqa | 0 | acc | 60.88 | _ | 1.14 | |
| | | piqa | 0 | acc\_norm | 60.55 | _ | 1.14 | |
| | | winogrande | 0 | acc | 50.91 | _ | 1.41 | |
| | Average: 40.01 |
| |
|
| | BigBench |
| |
|
| | | Task | Version | Metric | Value | Std Err | |
| | |-----------------------------------|---------|--------|--------|---------| |
| | | bigbench\_causal\_judgement | 0 | MCG | 50 | 2.26 | |
| | | bigbench\_date\_understanding | 0 | MCG | 49.14 | 2.18 | |
| | | bigbench\_disambiguation\_qa | 0 | MCG | 49.31 | 2.74 | |
| | | bigbench\_geometric\_shapes | 0 | MCG | 14.18 | 1.37 | |
| | | bigbench\_logical\_deduction\_5objs | 0 | MCG | 49.41 | 2.73 | |
| | | bigbench\_logical\_deduction\_7objs | 0 | MCG | 41.48 | 2.46 | |
| | | bigbench\_logical\_deduction\_3objs | 0 | MCG | 69.33 | 2.75 | |
| | | bigbench\_movie\_recommendation | 0 | MCG | 51.71 | 2.25 | |
| | | bigbench\_navigate | 0 | MCG | 50 | 1.58 | |
| | | bigbench\_reasoning\_colored\_obj | 0 | MCG | 51.92 | 0.99 | |
| | | bigbench\_ruin\_names | 0 | MCG | 48.14 | 2.01 | |
| | | bigbench\_salient\_trans\_err\_detec | 0 | MCG | 39.92 | 1.2 | |
| | | bigbench\_snarks | 0 | MCG | 64.14 | 3.71 | |
| | | bigbench\_sports\_understanding | 0 | MCG | 55.31 | 1.59 | |
| | | bigbench\_temporal\_sequences | 0 | MCG | 46.92 | 1.4 | |
| | | bigbench\_tsk\_shuff\_objs\_5 | 0 | MCG | 25.04 | 1.01 | |
| | | bigbench\_tsk\_shuff\_objs\_7 | 0 | MCG | 15.04 | 0.72 | |
| | | bigbench\_tsk\_shuff\_objs\_3 | 0 | MCG | 55.33 | 2.75 | |
| | Average: 44.75 |
| |
|
| | TruthfulQA |
| |
|
| | | Task | Version | Metric | Value | Std Err | |
| | |----------------------------------|---------|--------|--------|----------| |
| | | truthfulqa\_mc | 1 | mc1 | 30.11 | 1.61 | |
| | | truthfulqa\_mc | 1 | mc2 | 47.69 | 1.61 | |
| | Average: 38.90 |
| |
|
| |
|
| | # Openllm Benchmark |
| |
|
| | | Task |Version| Metric |Value| |Stderr| |
| | |-------------|------:|--------|----:|---|-----:| |
| | |arc_challenge| 0|acc |40.44|± | 1.43| |
| | | | |acc_norm|43.81|± | 1.34| |
| | |hellaswag | 0|acc |48.1 |± | 0.45| |
| | | | |acc_norm|62.73|± | 0.32| |
| | |gsm8k | 0|acc |5.6 |± | 0.6 | |
| | |winogrande | 0|acc |60.91|± | 1.3 | |
| | |mmlu | 0|acc |37.62 |±| 0.6 | |
| | |
| | Average: 73.5% |
| | |
| | ### TruthfulQA |
| | | Task |Version|Metric|Value| |Stderr| |
| | |-------------|------:|------|----:|---|-----:| |
| | |truthfulqa_mc| 1|mc1 |29.00|± | 1.58| |
| | | | |mc2 |45.83|± | 1.59| |
| |
|
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-07 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 8 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 100 |
| | - training_steps: 1300 |
| |
|
| |
|
| | ### 📝 Axolotl Configuration |
| |
|
| | ```yaml |
| | base_model: google/gemma-2b-it |
| | model_type: GemmaForCausalLM |
| | tokenizer_type: GemmaTokenizer |
| | trust_remote_code: true |
| | |
| | load_in_8bit: false |
| | load_in_4bit: true |
| | strict: false |
| | |
| | rl: dpo |
| | chat_template: chatml |
| | datasets: |
| | - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha |
| | split: train |
| | type: chatml.intel |
| | dataset_prepared_path: |
| | val_set_size: 0.01 |
| | output_dir: ./out |
| | |
| | adapter: qlora |
| | lora_model_dir: |
| | |
| | sequence_len: 1800 |
| | sample_packing: false |
| | pad_to_sequence_len: false |
| | |
| | lora_r: 16 |
| | lora_alpha: 16 |
| | lora_dropout: 0.05 |
| | lora_target_linear: true |
| | lora_fan_in_fan_out: |
| | lora_target_modules: |
| | |
| | wandb_project: gemma |
| | wandb_entity: |
| | wandb_watch: |
| | wandb_name: |
| | wandb_log_model: |
| | |
| | gradient_accumulation_steps: 8 |
| | micro_batch_size: 1 |
| | num_epochs: 1 |
| | optimizer: paged_adamw_32bit |
| | lr_scheduler: cosine |
| | learning_rate: 5e-7 |
| | |
| | train_on_inputs: false |
| | group_by_length: false |
| | bf16: true |
| | fp16: false |
| | tf32: true |
| | |
| | gradient_checkpointing: true |
| | early_stopping_patience: |
| | resume_from_checkpoint: |
| | local_rank: |
| | logging_steps: 1 |
| | xformers_attention: |
| | flash_attention: false |
| | |
| | warmup_steps: 100 |
| | evals_per_epoch: 1 |
| | eval_table_size: |
| | eval_table_max_new_tokens: 128 |
| | save_steps: 1000 |
| | max_steps: 1300 |
| | debug: |
| | deepspeed: |
| | weight_decay: 0.0 |
| | fsdp: |
| | fsdp_config: |
| | special_tokens: |
| | ``` |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.0.dev0 |
| | - Pytorch 2.1.2+cu118 |
| | - Datasets 2.17.0 |
| | - Tokenizers 0.15.0 |
| | - axolotl: 0.4.0 |
| |
|
| | [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
