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--- |
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base_model: winglian/Llama-3-8b-64k-PoSE |
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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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- 64k |
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- pose |
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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-3-8B-Instruct-64k |
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quantized_by: MaziyarPanahi |
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datasets: |
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- Intel/orca_dpo_pairs |
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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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# MaziyarPanahi/Llama-3-8B-Instruct-64k |
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This model has been made based on a great of [@winglian](https://huggingface.co/winglian/) with his latest model [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE/) |
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> This model uses [PoSE](https://huggingface.co/papers/2309.10400) to extend Llama's context length from 8k to 64k @ rope_theta: 500000.0. |
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> We used PoSE with continued pretraining on 300M tokens from the RedPajama V1 dataset using data between 6k-8k tokens. |
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> We have further set rope_theta to 2M after continued pre-training to potentially further extend the context past 64k. |
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> This was trained on a subset of the RedPajama v1 dataset with text between 6k-8k context. We trained a rank stabilized LoRA of rank 256. [WandB](https://wandb.ai/oaaic/llama-3-64k/runs/tkcyjt37) |
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# Quantized GGUF |
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All GGUF models come with context length of `64000`: [MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF) |
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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-64k" |
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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=8192, |
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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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``` |
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