Create README.md

README.md

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+---
+base_model: teknium/OpenHermes-2.5-Mistral-7B
+tags:
+- mistral
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+- dpo
+- rlhf
+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 based on the [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 most benchmarks (see results).
+
+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.
+
+The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/1h4tAJStIef_BcO-OkY97X9_OFgKnFrLl). It required an A100 GPU for about an hour.
+
+## Quantized models
+
+* **GGUF**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
+* **AWQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
+* **GPTQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ
+* **EXL2**:
+  * 3.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2
+  * 4.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2
+  * 5.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2
+  * 6.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2
+  * 8.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2
+
+## Results
+
+**Update:** NeuralHermes-2.5 became the best Hermes-based model on the Open LLM leaderboard and one of the very best 7b models. 🎉
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/yWe6VBFxkHiuOlDVBXtGo.png)
+
+Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
+
+Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
+
+### AGIEval
+![](https://i.imgur.com/7an3B1f.png)
+
+### GPT4All
+![](https://i.imgur.com/TLxZFi9.png)
+
+### TruthfulQA
+![](https://i.imgur.com/V380MqD.png)
+
+You can view the Weights & Biases report [here](https://api.wandb.ai/links/halbihn/uem1q2dj).
+
+## 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
+
+model_id = "halbihn/NeuralHermes-2.5-Mistral-7B"
+
+# 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(model_id)
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    tokenizer=tokenizer
+)
+
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+response = sequences[0]['generated_text'].split("<|im_start|>assistant")[-1].strip()
+print(response)
+
+
+# streaming example
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+
+model_id = "halbihn/NeuralHermes-2.5-Mistral-7B"
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+def stream(
+  user_prompt: str,
+  max_tokens: int = 200,
+) -> None:
+  """Text streaming example
+  """
+  
+  system_prompt = 'Below is a conversation between Human and AI assistant named Mistral\n'
+
+  message = [
+      {"role": "system", "content": system_prompt},
+      {"role": "user", "content": user_prompt}
+  ]
+  prompt = tokenizer.apply_chat_template(
+    message, 
+    add_generation_prompt=True, 
+    tokenize=False,
+  )
+
+  inputs = tokenizer([prompt], return_tensors="pt").to(device)
+
+  streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+  _ = model.generate(**inputs, streamer=streamer, max_new_tokens=max_tokens)
+
+stream("Tell me about the future")
+
+>>> The future is a vast and uncertain expanse, shaped by the collective actions and innovations of humanity. It is a blend of possibilities, technological advancements, and societal changes. Some potential aspects of the future include:
+>>> 
+>>> 1. Technological advancements: Artificial intelligence, quantum computing, and biotechnology are expected to continue evolving, leading to breakthroughs in fields like medicine, energy, and communication.
+>>> 
+>>> 2. Space exploration: As technology progresses, space travel may become more accessible, enabling humans to establish colonies on other planets and explore the cosmos further.
+>>> 
+>>> 3. Climate change mitigation: The future will likely see increased efforts to combat climate change through renewable energy sources, carbon capture technologies, and sustainable practices.
+>>> 
+>>> 4. Artificial intelligence integration: AI will likely become more integrated into daily life, assisting with tasks, automating jobs, and even influencing decision-making processes in various industries.
+```
+
+## 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