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README.md
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---
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language:
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- en
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license: mit
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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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- microsoft
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- phi
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- pytorch
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- phi-3
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- nlp
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- code
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- chatml
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base_model: microsoft/Phi-3-mini-4k-instruct
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model_name: Phi-3-mini-4k-instruct-v0.3
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pipeline_tag: text-generation
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inference: false
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model_creator: MaziyarPanahi
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quantized_by: MaziyarPanahi
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---
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<img src="./phi-3-instruct.webp" alt="Phi-3 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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# MaziyarPanahi/Phi-3-mini-4k-instruct-v0.3
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This model is a fine-tune (DPO) of `microsoft/Phi-3-mini-4k-instruct` model.
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# ⚡ Quantized GGUF
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coming soon
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# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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coming soon
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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/Phi-3-mini-4k-instruct-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/Phi-3-mini-4k-instruct-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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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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# this should work perfectly for the model to stop generating
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terminators = [
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tokenizer.eos_token_id, # this should be <|im_end|>
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tokenizer.convert_tokens_to_ids("<|assistant|>"), # sometimes model stops generating at <|assistant|>
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tokenizer.convert_tokens_to_ids("<|end|>") # sometimes model stops generating at <|end|>
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]
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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)
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generation_args = {
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"max_new_tokens": 500,
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"return_full_text": False,
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"temperature": 0.0,
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"do_sample": False,
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"streamer": streamer,
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"eos_token_id": terminators,
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}
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output = pipe(messages, **generation_args)
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print(output[0]['generated_text'])
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```
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