metadata

language:
  - en
license: apache-2.0
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
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: 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
model-index:
  - name: Minueza-32M-Chat
    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: 20.39
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          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: 26.54
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          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: 25.75
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          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: 47.27
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          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: 50.99
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          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
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
          name: Open LLM Leaderboard

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"])

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