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--- |
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base_model: teknium/OpenHermes-2.5-Mistral-7B |
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tags: |
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- mistral |
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- instruct |
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- finetune |
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- chatml |
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- gpt4 |
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- synthetic data |
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- distillation |
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- dpo |
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- rlhf |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- mlabonne/chatml_dpo_pairs |
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--- |
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A variation/copy of NeuralHermes 2.5 - Mistral 7B |
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This is a variation of NeuralHermes which is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the mlabonne/chatml_dpo_pairs dataset. It surpasses the original model on most benchmarks (see results). |
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It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template. |
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The code to train this model is available on Google Colab and GitHub. It required an A100 GPU for about an hour. |
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I have used the following code to train the [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour. |
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Copied from NeuralHermes-2.5-Mistral-7B: |
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## Quantized models |
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* **GGUF**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF |
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* **AWQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ |
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* **GPTQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ |
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* **EXL2**: |
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* 3.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2 |
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* 4.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2 |
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* 5.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2 |
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* 6.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2 |
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* 8.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2 |
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## Usage |
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You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend. |
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You can also run this model using the following code: |
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```python |
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import transformers |
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from transformers import AutoTokenizer |
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# Format prompt |
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message = [ |
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{"role": "system", "content": "You are a helpful assistant chatbot."}, |
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{"role": "user", "content": "What is a Large Language Model?"} |
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] |
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tokenizer = AutoTokenizer.from_pretrained(new_model) |
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) |
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# Create pipeline |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=new_model, |
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tokenizer=tokenizer |
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) |
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# Generate text |
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sequences = pipeline( |
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prompt, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9, |
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num_return_sequences=1, |
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max_length=200, |
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) |
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print(sequences[0]['generated_text']) |
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``` |
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## Training hyperparameters |
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**LoRA**: |
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* r=16 |
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* lora_alpha=16 |
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* lora_dropout=0.05 |
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* bias="none" |
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* task_type="CAUSAL_LM" |
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* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] |
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**Training arguments**: |
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* per_device_train_batch_size=4 |
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* gradient_accumulation_steps=4 |
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* gradient_checkpointing=True |
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* learning_rate=5e-5 |
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* lr_scheduler_type="cosine" |
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* max_steps=5 |
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* optim="paged_adamw_32bit" |
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* warmup_steps=100 |
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**DPOTrainer**: |
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* beta=0.1 |
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* max_prompt_length=1024 |
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* max_length=1536 |
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* |
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--- |
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license: mit |
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language: |
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- en |
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--- |
