---
language:
  - en
license: apache-2.0
library_name: mlx
tags:
  - mlx
  - apple-silicon
  - moe
  - mixture-of-experts
  - 4-bit
  - quantized
  - gpt-oss
  - context-retrieval
base_model: chromadb/context-1
pipeline_tag: text-generation
model-index:
  - name: context-1-MLX-MXFP4
    results:
      - task:
          type: text-generation
        metrics:
          - name: Tokens per second (M1 Max)
            type: throughput
            value: 69
          - name: Peak Memory (GB)
            type: memory
            value: 12
---

# chromadb/context-1 MLX MXFP4

This model was converted from [chromadb/context-1](https://huggingface.co/chromadb/context-1) to MLX format with MXFP4 (4-bit) quantization for efficient inference on Apple Silicon.

## Model Description

- **Base Model**: [chromadb/context-1](https://huggingface.co/chromadb/context-1) (fine-tuned from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b))
- **Architecture**: 20B parameter Mixture of Experts (MoE) with 32 experts, 4 active per token
- **Format**: MLX with MXFP4 quantization
- **Quantization**: 4.504 bits per weight

## Performance (Apple M1 Max, 64GB)

| Metric | Value |
|--------|-------|
| Model Size | 11 GB |
| Peak Memory | 12 GB |
| Generation Speed | ~69 tokens/sec |
| Prompt Processing | ~70 tokens/sec |
| Latency | ~14.5 ms/token |

## Usage

```python
from mlx_lm import load, generate

model, tokenizer = load("foadmk/context-1-MLX-MXFP4")
response = generate(model, tokenizer, prompt="What is the capital of France?", max_tokens=100, verbose=True)
```

## Conversion Notes

The chromadb/context-1 model uses a different weight format than the original openai/gpt-oss-20b, which required custom conversion logic:

### Key Differences from Original Format
- **Dense BF16 tensors** (not quantized blocks with `_blocks` suffix)
- **gate_up_proj shape**: `(experts, hidden, intermediate*2)` with interleaved gate/up weights

### Weight Transformations Applied

1. **gate_up_proj** `(32, 2880, 5760)`:
   - Transpose to `(32, 5760, 2880)`
   - Interleaved split: `[:, ::2, :]` for gate, `[:, 1::2, :]` for up
   - Result: `gate_proj.weight` and `up_proj.weight` each `(32, 2880, 2880)`

2. **down_proj** `(32, 2880, 2880)`:
   - Transpose to match MLX expected format

3. **Bypass mlx_lm sanitize**: Pre-naming weights with `.weight` suffix to skip incorrect splitting

### Conversion Script

A conversion script is included in this repo: `convert_context1_to_mlx.py`

```bash
python convert_context1_to_mlx.py --output ./context1-mlx-mxfp4
```

## Intended Use

This model is optimized for:
- Context-aware retrieval and search tasks
- Running locally on Apple Silicon Macs
- Low-latency inference without GPU requirements

## Limitations

- Requires Apple Silicon Mac with MLX support
- Best performance on M1 Pro/Max/Ultra or newer with 32GB+ RAM
- Model outputs structured JSON-like responses (inherited from base model training)

## Citation

If you use this model, please cite the original:

```bibtex
@misc{chromadb-context-1,
  author = {Chroma},
  title = {Context-1: A Fine-tuned GPT-OSS Model for Retrieval},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/chromadb/context-1}
}
```

## Acknowledgments

- [chromadb](https://github.com/chroma-core/chroma) for the original context-1 model
- [OpenAI](https://openai.com) for the gpt-oss-20b base model
- [Apple MLX team](https://github.com/ml-explore/mlx) for the MLX framework
- [mlx-community](https://huggingface.co/mlx-community) for MLX model conversion tools