metadata
license: apache-2.0
datasets:
- Intel/orca_dpo_pairs
- nvidia/HelpSteer
- jondurbin/truthy-dpo-v0.1
language:
- en
library_name: transformers
pipeline_tag: text-generation
Yi-9B-Forest-DPO
Introducing Yi-9B-Forest-DPO, a LLM fine-tuned with base model 01-ai/Yi-9B, using direct preference optimization. This model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks.
A mixture of the following datasets was used for fine-tuning.
- Intel/orca_dpo_pairs
- nvidia/HelpSteer
- jondurbin/truthy-dpo-v0.1
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abhishekchohan/Yi-9B-Forest-DPO"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])