language:

• en license: apache-2.0 tags:
• pretrained datasets:
• Skylion007/openwebtext
• c4
• wikimedia/wikipedia
• tiiuae/falcon-refinedweb
• izumi-lab/open-text-books
• togethercomputer/RedPajama-Data-V2
• databricks/databricks-dolly-15k
• euclaise/reddit-instruct-curated
• CohereForAI/aya_dataset pipeline_tag: text-generation widget:
• messages:
  • role: user content: Specs of a game about trolls and warriors in a fantasy world.
• messages:
  • role: user content: Reducing waste generation is essential to...
• messages:
  • role: user content: Water, planet, resource, future
• messages:
  • role: user content: >- Background story of an RPG game about wizards and dragons in a sci-fi world. The story takes place in a... 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-Base 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: 21.33 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base 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: 26.39 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base 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: 24.8 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base 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: 47.45 source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base 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: 53.2 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base 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: 0.38 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Base name: Open LLM Leaderboard new_version: Felladrin/Minueza-2-96M

Minueza-32M-Base

Summary

Minueza-32M-Base is a foundation model with 32 million parameters trained from scratch on a large corpus of text in English.

It's available in the following formats: Safetensors, GGUF, and ONNX.

And it's being released alongside some fine-tuned versions:

• Minueza-32M-UltraChat: Trained on a single conversational dataset.
• Minueza-32M-Chat: Trained on a mix of conversational datasets.
• Minueza-32Mx2-Chat: Sparse Mixture of Experts trained on interleaved conversational datasets.
• And more...

Intended Uses

This model was created with the following objectives in mind:

• Run on mobile web browsers via Transformers.js.
• Run fast on machines without GPU.
• Serve as a base for fine-tunes using ChatML format, hence the two additional special tokens (<|im_start|> and <|im_end|>) with <|im_end|> as default EOS token.
  • ChatML works great for both instruction and chat models, so if all fine-tunes are made following the ChatML pattern, other users might benefit from the easiness of creating merges.

Datasets

The model was trained on a subset of each of the following non-synthetic datasets:

• Skylion007/openwebtext
• c4
• wikimedia/wikipedia - 20231101.simple
• tiiuae/falcon-refinedweb
• izumi-lab/open-text-books
• togethercomputer/RedPajama-Data-V2
• databricks/databricks-dolly-15k
• euclaise/reddit-instruct-curated
• CohereForAI/aya_dataset - original english annotations

The subsets were interleaved to form the final training corpus of approximately 650 million tokens.

Model Architecture

This is a transformer model with the Mistral architecture, trained on a context window of 2048 tokens.

The pretraining was made with these hyperparameters and frameworks:

Usage

This is just a base model. For your task, you will likely want to perform application-specific fine-tuning as recommended above.

Also note that this model was trained on internet text data, which may contain biases, offensive or inappropriate content, and may produce incorrect or irrelevant responses. No evaluation has been conducted, so use with care.

Having that said, here's how you can run it:

from transformers import pipeline

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

prompt = "The best way to improve your health is"

output = generate(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.72,
    top_p=0.73,
    top_k=50,
    repetition_penalty=1.176,
)

print(output[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

License

This model is licensed under the Apache License 2.0.