metadata

license: apache-2.0
datasets:
  - argilla/distilabel-intel-orca-dpo-pairs
base_model: sethuiyer/Chikuma_10.7B
library_name: transformers
pipeline_tag: text-generation
tags:
  - dpo

Chikuma_10.7B - V2 (Enhanced with DPO)

Chikuma

This model is the DPO fine tuned version of Chikuma_10.7B, which was a depth upscaled merge of:

The name "Chikuma" is inspired by the Chikuma River, the longest in Japan, known for its continuous flow and meandering path. This metaphorically represents the model's depth, fluidity, and adaptability in processing and understanding language.

Dataset used for Fine Tuning

Dataset: /argilla/distilabel-intel-orca-dpo-pairs

The dataset was roughly ~3000 samples but they were high quality (according to the chosen_score).

The following filters were applied to the original dataset:

dataset = dataset.filter(
    lambda r:
        r["status"] != "tie" and
        r["chosen_score"] >= 8 and
        not r["in_gsm8k_train"]
)

Chat Template

The chat template for Chikuma_10.7B - V2 is a modified version of ChatML, optimized for improved interaction and engagement:

<|im_start|>GPT4 Correct system:
{system} Always use <|end_of_turn|> when you want to end the answer. <|im_end|>
<|im_start|>GPT4 Correct user:
{user}<|im_end|>
<|im_start|>GPT4 Correct Assistant:
{asistant}<|im_end|>

Nous Benchmark Evaluation

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
SynthIQ-7b	42.67	73.71	56.51	44.59	54.37
openchat/openchat-3.5-0106	44.17	73.72	52.53	44.4	53.71
Chikuma_10.7B	42.41	73.41	56.69	43.5	54.00
distilabled_Chikuma_10.7B	42.77	73.81	58.83	44.83	55.06

OpenLLM Leaderboard

Benchmark Name	Performance
ARC	66.38
HellaSwag	85
MMLU	65.27
TruthfulQA	58.83
Winogrande	78.77
GSM8K	63.68
Average	69.65

Training Environment

Hardware: Single A100 80GB GPU in a runpod, utilized for approximately 1.5 hours.
Training Script: Accessible via Google Colab Notebook. Special thanks to mlabonne for providing the template.

Usage

# Format prompt
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(new_model)

# Create pipeline
pipeline = transformers.pipeline(
    "text-generation",
    model=new_model,
    tokenizer=tokenizer,
    device="cuda"
)

# Generate text

message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "Who invented LLMs?"}
]

prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

sequences = pipeline(
    prompt,
    max_new_tokens=512
)
print(sequences[0]['generated_text'])

Acknowledgements

A heartfelt appreciation goes to the vibrant open-source community, particularly:

The Intel team for publishing a great open dataset and show how well it worked in the first place
Teknium and NousResearch for their awesome work and models.
Maxime for sharing such great resources.
Argilla for publishing argilla/distilabel-intel-orca-dpo-pairs