| --- |
| license: cc-by-nc-4.0 |
| base_model: google/gemma-7b-it |
| tags: |
| - generated_from_trainer |
| - axolotl |
| - gemma |
| - instruct |
| - finetune |
| - chatml |
| - gpt4 |
| - synthetic data |
| - distillation |
| model-index: |
| - name: gemma-7b-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-7b-openhermes |
|
|
|
|
|
|
|  |
|
|
| gemma-7b-openhermes is a variant of the Gemma 7B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset |
| using QLoRA. |
|
|
|
|
| * [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-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-7b-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 | 24.80 | _ | 2.72 | |
| | agieval\_aqua\_rat | 0 | acc\_norm | 24.80 | _ | 2.72 | |
| | agieval\_logiqa\_en | 0 | acc | 20.89 | _ | 1.59 | |
| | agieval\_logiqa\_en | 0 | acc\_norm | 23.35 | _ | 1.66 | |
| | agieval\_lsat\_ar | 0 | acc | 21.74 | _ | 2.73 | |
| | agieval\_lsat\_ar | 0 | acc\_norm | 20.43 | _ | 2.66 | |
| | agieval\_lsat\_lr | 0 | acc | 15.49 | _ | 1.60 | |
| | agieval\_lsat\_lr | 0 | acc\_norm | 20.59 | _ | 1.79 | |
| | agieval\_lsat\_rc | 0 | acc | 17.10 | _ | 2.30 | |
| | agieval\_lsat\_rc | 0 | acc\_norm | 17.84 | _ | 2.34 | |
| | agieval\_sat\_en | 0 | acc | 29.61 | _ | 3.19 | |
| | agieval\_sat\_en | 0 | acc\_norm | 29.61 | _ | 3.19 | |
| | agieval\_sat\_en\_without\_passage | 0 | acc | 26.21 | _ | 3.07 | |
| | agieval\_sat\_en\_without\_passage | 0 | acc\_norm | 24.76 | _ | 3.01 | |
| | agieval\_sat\_math | 0 | acc | 22.73 | _ | 2.83 | |
| | agieval\_sat\_math | 0 | acc\_norm | 22.73 | _ | 2.83 | |
| Average: 22.29 |
|
|
| GPT4ALL |
|
|
| | Task | Version | Metric | Value | | StdErr | |
| |---------------|---------|------------|---------|---|-------------| |
| | arc_challenge | 0 | acc | 20.14 | _ | 1.17 | |
| | arc_challenge | 0 | acc_norm | 22.87 | _ | 1.23 | |
| | arc_easy | 0 | acc | 32.37 | _ | 0.96 | |
| | arc_easy | 0 | acc_norm | 31.61 | _ | 0.95 | |
| | boolq | 1 | acc | 45.78 | _ | 0.87 | |
| | hellaswag | 0 | acc | 32.03 | _ | 0.47 | |
| | hellaswag | 0 | acc_norm | 35.18 | _ | 0.48 | |
| | openbookqa | 0 | acc | 17.8 | _ | 1.71 | |
| | openbookqa | 0 | acc_norm | 29.8 | _ | 2.05 | |
| | piqa | 0 | acc | 54.46 | _ | 1.16 | |
| | piqa | 0 | acc_norm | 54.57 | _ | 1.16 | |
| | winogrande | 0 | acc | 48.30 | _ | 1.40 | |
| Average: 32.00 |
|
|
|
|
| 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 |48.12|± | 1.46| |
| | | |acc_norm|51.27|± | 1.46| |
| |hellaswag | 0|acc |55.4 |± | 0.49| |
| | | |acc_norm|71.92|± | 0.42| |
| |gsm8k | 0|acc |29.87|± | 1.2 | |
| |winogrande | 0|acc |68.19|± | 1.3 | |
| |mmlu | 0|acc |53.62 |±| 0.6 | |
| |
| Average: 73.5% |
| |
| ### TruthfulQA |
| | Task |Version|Metric|Value| |Stderr| |
| |-------------|------:|------|----:|---|-----:| |
| |truthfulqa_mc| 1|mc1 |30.23|± | 1.60| |
| | | |mc2 |47.17|± | 1.63| |
|
|
|
|
|
|
| ### 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: 1000 |
|
|
|
|
| ### 📝 Axolotl Configuration |
|
|
| ```yaml |
| base_model: google/gemma-7b-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: 1000 |
| 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) |
