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README.md CHANGED
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  ---
 
 
 
 
 
 
 
 
 
 
 
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  license: apache-2.0
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ <center><img src="https://i.imgur.com/qIhaFNM.png"></center>
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+
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+ # NeuralHermes 2.5 - Mistral 7B
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+
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+ NeuralHermes is an [teknium/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 surpasses the original model on several benchmarks (see results).
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+
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+ It is directly inspired by the RLHF process described by [Intel/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.
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+
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+ 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.
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+
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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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+ * EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
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+
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+ ## Results
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+
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+ Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
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+
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+ Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
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+
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+ ### AGIEval
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+ ![](https://i.imgur.com/7an3B1f.png)
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+
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+ ### GPT4All
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+ ![](https://i.imgur.com/TLxZFi9.png)
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+
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+ ### TruthfulQA
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+ ![](https://i.imgur.com/V380MqD.png)
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+
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+ You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
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+
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+ ## Usage
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+
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+ You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
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+
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+ You can also run this model using the following code:
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+
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+ ```python
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+ import transformers
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+ from transformers import AutoTokenizer
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+
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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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+
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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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+
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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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+
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+
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+ ## Training hyperparameters
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+
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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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+
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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=200
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+ * optim="paged_adamw_32bit"
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+ * warmup_steps=100
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+
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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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+ "torch_dtype": "float16",
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+ "vocab_size": 32002
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+ }
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+ url: https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B
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+ branch: main
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+ download date: 2023-11-30 10:34:00
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