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URL: https://huggingface.co/Felladrin/Minueza-32M-Chat

⇱ Felladrin/Minueza-32M-Chat · Hugging Face

Minueza-32M-Chat: 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 

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

Usage Example 

from transformers import pipeline

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

messages = [
 {
 "role": "system",
 "content": "You are a helpful assistant who answers the user's questions with details and curiosity.",
 },
 {
 "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 SFT Trainer and DPO Trainer, in several sessions, using the following settings:

For Supervised Fine-Tuning:

Hyperparameter Value
learning_rate 2e-5
total_train_batch_size 24
max_seq_length 2048
weight_decay 0
warmup_ratio 0.02

For Direct Preference Optimization:

Hyperparameter Value
learning_rate 7.5e-7
total_train_batch_size 6
max_length 2048
max_prompt_length 1536
max_steps 200
weight_decay 0
warmup_ratio 0.02
beta 0.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 28.49
AI2 Reasoning Challenge (25-Shot) 20.39
HellaSwag (10-Shot) 26.54
MMLU (5-Shot) 25.75
TruthfulQA (0-shot) 47.27
Winogrande (5-shot) 50.99
GSM8k (5-shot) 0.00
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Safetensors
Model size
32.8M params
Tensor type
F32
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Evaluation results