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Felladrin/Minueza-32M-UltraChat

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
6likes141downloads
Model Card

Minueza-32M-UltraChat: A chat model with 32 million parameters

Recommended Prompt Format

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant

Recommended Inference Parameters

yml
do_sample: true
temperature: 0.65
top_p: 0.55
top_k: 35
repetition_penalty: 1.176

Usage Example

python
from transformers import pipeline

generate = pipeline("text-generation", "Felladrin/Minueza-32M-UltraChat")

messages = [
    {
        "role": "system",
        "content": "You are a highly knowledgeable and friendly assistant. Your goal is to understand and respond to user inquiries with clarity. Your interactions are always respectful, helpful, and focused on delivering the most accurate information to the user.",
    },
    {
        "role": "user",
        "content": "Hey! Got a question for you!",
    },
    {
        "role": "assistant",
        "content": "Sure! What's it?",
    },
    {
        "role": "user",
        "content": "What are some potential applications for quantum computing?",
    },
]

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

output = generate(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.65,
    top_k=35,
    top_p=0.55,
    repetition_penalty=1.176,
)

print(output[0]["generated_text"])

How it was trained

This model was trained with SFTTrainer using the following settings:

HyperparameterValue
Learning rate2e-5
Total train batch size16
Max. sequence length2048
Weight decay0
Warmup ratio0.1
OptimizerAdam with betas=(0.9,0.999) and epsilon=1e-08
Schedulercosine
Seed42

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

MetricValue
Avg.28.97
AI2 Reasoning Challenge (25-Shot)21.08
HellaSwag (10-Shot)26.95
MMLU (5-Shot)26.08
TruthfulQA (0-shot)47.70
Winogrande (5-shot)51.78
GSM8k (5-shot)0.23