Introducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, 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

python

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "abhishekchohan/SOLAR-10.7B-Instruct-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"])

abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1

SOLAR-10.7B-Instruct-Forest-DPO

abhishekchohan/
SOLAR-10.7B-Instruct-Forest-DPO-v1