README.md

metadata

language:
  - en
license: mit
library_name: transformers
tags:
  - axolotl
  - finetune
  - dpo
  - microsoft
  - phi
  - pytorch
  - phi-3
  - nlp
  - code
  - chatml
base_model: microsoft/Phi-3-mini-4k-instruct
model_name: Phi-3-mini-4k-instruct-v0.1
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi

MaziyarPanahi/Phi-3-mini-4k-instruct-v0.1

This model is a fine-tune (DPO) of microsoft/Phi-3-mini-4k-instruct model.

⚡ Quantized GGUF

coming soon

🏆 Open LLM Leaderboard Evaluation Results

coming soon

Prompt Template

This model uses ChatML prompt template:

<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}

How to use

You can use this model by using MaziyarPanahi/Phi-3-mini-4k-instruct-v0.1 as the model name in Hugging Face's transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch

model_id = "MaziyarPanahi/Phi-3-mini-4k-instruct-v0.1"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    # attn_implementation="flash_attention_2"
)

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True
)

streamer = TextStreamer(tokenizer)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|im_end|>"),
    tokenizer.convert_tokens_to_ids("<|assistant|>"),
    tokenizer.convert_tokens_to_ids("<|end|>")
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
    "streamer": streamer,
    "eos_token_id": terminators,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])