---
license: apache-2.0
base_model:
- openai/gpt-oss-120b
tags:
- george_carlin_saving_the_planet
- gpt_oss_120b_carlin_analysis
- gpt_oss_120b_carlin_clone
- gpt_oss_120b_carlin_saving_the_planet_impersonation
---
# Model Card for GPT-OSS-120B

## Model Details

### Model Description
GPT-OSS-120B is a 120 billion parameter generative language model based on the transformer architecture. This model represents one of the largest openly available language models, designed for a wide range of natural language processing tasks including text generation, summarization, question answering, and creative content generation.

- **Developed by:** MLX Community
- **Model type:** Transformer-based language model
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from:** Base GPT architecture

## Uses

### Direct Use
The model can be used for:
- Text generation and completion
- Content summarization
- Question answering
- Creative writing and storytelling
- Code generation and explanation
- Educational content creation

### Downstream Use
The model can be fine-tuned for:
- Specialized domain applications
- Chatbots and conversational AI
- Content moderation
- Sentiment analysis
- Language translation

### Out-of-Scope Use
The model should not be used for:
- Generating harmful, abusive, or unethical content
- Medical or legal advice without human supervision
- Critical decision-making systems without human oversight
- Generating misinformation or fake content
- impersonation without consent

## Bias, Risks, and Limitations

GPT-OSS-120B may exhibit biases present in its training data. Users should be aware of potential issues including:

- Social, racial, and gender biases
- Political and cultural biases
- Factual inaccuracies in generated content
- Potential for generating plausible but incorrect information
- Sensitivity to prompt phrasing

### Recommendations
Users should:
- Verify important facts generated by the model
- Use human oversight for critical applications
- Consider potential biases when deploying the model
- Implement content filtering where appropriate

## How to Get Started with the Model

Use the code below to get started with the model:

```python
from mlx_lm import load, generate

# Load the model and tokenizer
model, tokenizer = load("mlx-community/gpt-oss-120b-MXFP4-Q4")

# Generate text
messages = [{"role": "user", "content": "Explain quantum computing in simple terms."}]
formatted_prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

response = generate(
    model,
    tokenizer,
    prompt=formatted_prompt,
    max_tokens=500,
    verbose=False
)
```

## Training Details

### Training Data
The model was trained on a diverse dataset of text from publicly available sources including:
- Web pages (Common Crawl)
- Books
- Academic papers
- Code repositories
- News articles

### Training Procedure
- **Architecture:** Transformer decoder
- **Parameters:** 120 billion
- **Precision:** 4-bit quantized (MXFP4-Q4)
- **Context length:** 2048 tokens

## Evaluation

### Results
The model demonstrates strong performance on:
- Language understanding tasks
- Creative writing
- Technical explanation
- Code generation
- Multi-step reasoning

### Evaluation Factors
- Perplexity on held-out test sets
- Human evaluation of generated content
- Task-specific benchmarks

## Environmental Impact

- **Hardware Type:** Apple Silicon (M-series)
- **Hours used:** Training details not specified
- **Cloud Provider:** Not applicable
- **Compute Region:** Not specified
- **Carbon Emitted:** Information not available

## Technical Specifications

### Model Architecture and Objective
GPT-OSS-120B uses a transformer decoder architecture with:
- 120 billion parameters
- 4-bit quantization
- Rotary positional embeddings
- Learned vocabulary of 50,000 tokens

### Compute Infrastructure
- **Hardware:** Optimized for Apple Silicon with MLX
- **Training Infrastructure:** Not specified

### Training Data
The model was trained on a diverse corpus of text data from publicly available sources.

## Citation

**BibTeX:**
```bibtex
@misc{gpt-oss-120b,
  title = {GPT-OSS-120B: A 120B Parameter Open Language Model},
  author = {MLX Community},
  year = {2024},
  howpublished = {\url{https://huggingface.co/mlx-community/gpt-oss-120b-MXFP4-Q4}},
}
```

## Glossary

- **Transformer:** Neural network architecture using self-attention mechanisms
- **Quantization:** Technique to reduce model size by using lower precision numbers
- **MLX:** Machine learning framework for Apple Silicon

## More Information

For more information about the model, training process, or usage guidelines, please refer to the documentation on the Hugging Face model page.

**Model Card Authors:** MLX Community

**Model Card Contact:** For questions about this model card, please use the discussion forum on the Hugging Face model page.