Minueza-32Mx2-Chat

• Base model: Felladrin/Minueza-32M-Base
• Datasets used during SFT:
  • [ChatML] databricks/databricks-dolly-15k
  • [ChatML] euclaise/reddit-instruct-curated
  • [ChatML] THUDM/webglm-qa
  • [ChatML] starfishmedical/webGPT_x_dolly
  • [ChatML] LDJnr/Capybara
  • [ChatML] Open-Orca/SlimOrca-Dedup
  • [ChatML] HuggingFaceH4/ultrachat_200k
  • [ChatML] nvidia/HelpSteer
  • [ChatML] sablo/oasst2_curated
  • [ChatML] CohereForAI/aya_dataset
• Datasets used during DPO:
  • [ChatML] argilla/distilabel-capybara-dpo-7k-binarized
  • [ChatML] argilla/distilabel-intel-orca-dpo-pairs
  • [ChatML] argilla/ultrafeedback-binarized-preferences
  • [ChatML] sablo/oasst2_dpo_pairs_en
  • [ChatML] NeuralNovel/Neural-DPO
• License: Apache License 2.0

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-32Mx2-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:

For Direct Preference Optimization:

Base model

Felladrin/Minueza-32M-Base

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They may be small, but they're training like giants! • 9 items • Updated Aug 16, 2025 • 20