Create README.md

README.md

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+---
+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
+datasets:
+- MaziyarPanahi/truthy-dpo-v0.1-axolotl
+model_name: Phi-3-mini-4k-instruct-v0.1
+pipeline_tag: text-generation
+inference: false
+model_creator: MaziyarPanahi
+quantized_by: MaziyarPanahi
+---
+
+<img src="./phi-3-instruct.webp" alt="Phi-3 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+
+# MaziyarPanahi/Phi-3-mini-4k-instruct-v0.1
+
+This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-70B-Instruct` model.
+
+# ⚡ Quantized GGUF
+
+coming soon
+
+# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+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.
+
+```python
+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'])
+
+
+```