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--- |
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base_model: meta-llama/Meta-Llama-3-70B-Instruct |
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library_name: transformers |
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tags: |
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- axolotl |
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- finetune |
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- dpo |
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- facebook |
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- meta |
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- pytorch |
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- llama |
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- llama-3 |
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- chatml |
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language: |
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- en |
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pipeline_tag: text-generation |
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license: llama3 |
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license_name: llama3 |
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license_link: LICENSE |
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inference: false |
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model_creator: MaziyarPanahi |
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model_name: Llama-3-70B-Instruct-DPO-v0.1 |
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quantized_by: MaziyarPanahi |
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datasets: |
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- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha |
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--- |
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<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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# Llama-3-8B-Instruct-DPO-v0.3 (32k) |
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This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-8B-Instruct` model. I have used `rope_theta` to extend the context length up to 32K safely. |
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# Quantized GGUF |
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All GGUF models come with context length of `32000`: [Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF) |
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# Prompt Template |
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This model uses `ChatML` prompt template: |
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``` |
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<|im_start|>system |
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{System} |
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<|im_end|> |
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<|im_start|>user |
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{User} |
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<|im_end|> |
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<|im_start|>assistant |
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{Assistant} |
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```` |
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# How to use |
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You can use this model by using `MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3` as the model name in Hugging Face's |
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transformers library. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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from transformers import pipeline |
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import torch |
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model_id = "MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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trust_remote_code=True, |
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# attn_implementation="flash_attention_2" |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_id, |
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trust_remote_code=True |
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) |
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streamer = TextStreamer(tokenizer) |
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pipeline = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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streamer=streamer |
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) |
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# Then you can use the pipeline to generate text. |
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messages = [ |
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, |
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{"role": "user", "content": "Who are you?"}, |
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] |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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terminators = [ |
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tokenizer.eos_token_id, |
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tokenizer.convert_tokens_to_ids("<|im_end|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=2048, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.95, |
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) |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
