Create README.md

README.md

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+---
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- mistral
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+- dpo
+- rlhf
+model-index:
+- name: OpenHermes-2-Mistral-7B
+  results: []
+license: apache-2.0
+language:
+- en
+datasets:
+- mlabonne/chatml_dpo_pairs
+---
+
+<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
+
+# NeuralHermes 2.5 - Mistral 7B
+
+NeuralHermes is an [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset.
+
+It is directly inspired by the RLHF process described by [neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template. I haven't performed a comprehensive evaluation of the model, but it works great, nothing broken apparently! :)
+
+The code to train this model is available on [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.
+
+GGUF versions of this model are available here: [mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF).
+
+## Usage
+
+You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
+
+You can also run this model using the following code:
+
+```python
+import transformers
+from transformers import AutoTokenizer
+
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful assistant chatbot."},
+    {"role": "user", "content": "What is a Large Language Model?"}
+]
+tokenizer = AutoTokenizer.from_pretrained(new_model)
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=new_model,
+    tokenizer=tokenizer
+)
+
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+print(sequences[0]['generated_text'])
+```
+
+
+## Training hyperparameters
+
+**LoRA**:
+* r=16,
+* lora_alpha=16,
+* lora_dropout=0.05,
+* bias="none",
+* task_type="CAUSAL_LM",
+* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
+
+**Training arguments**:
+* per_device_train_batch_size=4,
+* gradient_accumulation_steps=4,
+* gradient_checkpointing=True,
+* learning_rate=5e-5,
+* lr_scheduler_type="cosine",
+* max_steps=200,
+* optim="paged_adamw_32bit",
+* warmup_steps=100,
+
+**DPOTrainer**:
+* beta=0.1,
+* max_prompt_length=1024,
+* max_length=1536,