metadata

license: apache-2.0
base_model: Felladrin/Minueza-32M-Base
pipeline_tag: text-generation
language:
  - en
datasets:
  - databricks/databricks-dolly-15k
  - Felladrin/ChatML-databricks-dolly-15k
  - euclaise/reddit-instruct-curated
  - Felladrin/ChatML-reddit-instruct-curated
  - THUDM/webglm-qa
  - Felladrin/ChatML-WebGLM-QA
  - starfishmedical/webGPT_x_dolly
  - Felladrin/ChatML-webGPT_x_dolly
  - LDJnr/Capybara
  - Felladrin/ChatML-Capybara
  - Open-Orca/SlimOrca-Dedup
  - Felladrin/ChatML-SlimOrca-Dedup
  - HuggingFaceH4/ultrachat_200k
  - Felladrin/ChatML-ultrachat_200k
  - nvidia/HelpSteer
  - Felladrin/ChatML-HelpSteer
  - sablo/oasst2_curated
  - Felladrin/ChatML-oasst2_curated
  - CohereForAI/aya_dataset
  - Felladrin/ChatML-aya_dataset
  - argilla/distilabel-capybara-dpo-7k-binarized
  - Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized
  - argilla/distilabel-intel-orca-dpo-pairs
  - Felladrin/ChatML-distilabel-intel-orca-dpo-pairs
  - argilla/ultrafeedback-binarized-preferences
  - Felladrin/ChatML-ultrafeedback-binarized-preferences
  - sablo/oasst2_dpo_pairs_en
  - Felladrin/ChatML-oasst2_dpo_pairs_en
  - NeuralNovel/Neural-DPO
  - Felladrin/ChatML-Neural-DPO
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: system
        content: >-
          You are a highly knowledgeable assistant. Help the user as much as you
          can.
      - role: user
        content: How can I become a healthier person?
  - messages:
      - role: system
        content: You are a helpful assistant who gives creative responses.
      - role: user
        content: Write the specs of a game about mages in a fantasy world.
  - messages:
      - role: system
        content: You are a helpful assistant who answers user's questions with details.
      - role: user
        content: Tell me about the pros and cons of social media.
  - messages:
      - role: system
        content: >-
          You are a helpful assistant who answers user's questions with details
          and curiosity.
      - 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

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

Base model: Felladrin/Minueza-32M-Base
Datasets used during SFT:
Datasets used during DPO:
License: Apache License 2.0
Availability in other ML formats:
- GGUF: Felladrin/gguf-Minueza-32M-Chat
- ONNX: Felladrin/onnx-Minueza-32M-Chat

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"])