language:

• en license: apache-2.0 datasets:
• HuggingFaceH4/ultrachat_200k
• Felladrin/ChatML-ultrachat_200k base_model: Felladrin/Minueza-32M-Base pipeline_tag: text-generation widget:
• messages:
  • role: system content: You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields.
  • role: user content: Heya!
  • role: assistant content: Hi! How may I help you?
  • role: user content: I am interested in developing a career in software engineering. What would you recommend me to do?
• messages:
  • role: user content: Morning!
  • role: assistant content: Good morning! How can I help you today?
  • role: user content: Could you give me some tips for becoming a healthier person?
• messages:
  • role: user content: Write the specs of a game about mages in a fantasy world.
• messages:
  • role: user content: Tell me about the pros and cons of social media.
• 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? inference: parameters: max_new_tokens: 250 do_sample: true temperature: 0.65 top_p: 0.55 top_k: 35 repetition_penalty: 1.176 model-index:
• name: Minueza-32M-UltraChat results:
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 21.08 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 26.95 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 26.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 47.7 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 51.78 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 0.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-UltraChat name: Open LLM Leaderboard

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

• Base model: Felladrin/Minueza-32M-Base
• Dataset: [ChatML] HuggingFaceH4/ultrachat_200k
• License: Apache License 2.0
• Availability in other ML formats:
  • GGUF: Felladrin/gguf-Minueza-32M-UltraChat
  • ONNX: Felladrin/onnx-Minueza-32M-UltraChat

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-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:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here