Supra 50M Instruct
Collection
4 items • Updated
How to use sahilchachra/supra-50m-instruct-fp16-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir supra-50m-instruct-fp16-mlx sahilchachra/supra-50m-instruct-fp16-mlx
How to use sahilchachra/supra-50m-instruct-fp16-mlx with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16 # Run inference directly in the terminal: llama cli -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16 # Run inference directly in the terminal: llama cli -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16 # Run inference directly in the terminal: ./llama-cli -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sahilchachra/supra-50m-instruct-fp16-mlx:F16
docker model run hf.co/sahilchachra/supra-50m-instruct-fp16-mlx:F16
How to use sahilchachra/supra-50m-instruct-fp16-mlx with Ollama:
ollama run hf.co/sahilchachra/supra-50m-instruct-fp16-mlx:F16
How to use sahilchachra/supra-50m-instruct-fp16-mlx with Docker Model Runner:
docker model run hf.co/sahilchachra/supra-50m-instruct-fp16-mlx:F16
How to use sahilchachra/supra-50m-instruct-fp16-mlx with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sahilchachra/supra-50m-instruct-fp16-mlx:F16
lemonade run user.supra-50m-instruct-fp16-mlx-F16
lemonade list
MLX quantization of SupraLabs/Supra-50M-Instruct for Apple Silicon.
Variant: BFloat16 (lossless reference)
Disk size: 201 MB
Quantized by: sahilchachra
Evaluated on Apple M4 Pro with MLX. Model loaded once; performance and quality measured in a single pass.
| This model | FP16 baseline | |
|---|---|---|
| Decode tok/s (avg, long traces) | 1270.13 | 1270.13 |
| Peak memory (GB) | 0.223 | 0.223 |
| Disk size (MB) | 201 | 201 |
| Benchmark | This model | FP16 baseline | n |
|---|---|---|---|
| IFEval (instruction following) | 15.9% | 15.9% | 44 |
| Alpaca-cleaned (instruct F1 vs reference) | 36.2 | 36.2 | 50 |
| Context length | Decode tok/s |
|---|---|
| ~128 tokens | 1294.4 |
| ~256 tokens | 1274.3 |
| ~512 tokens | 1277.6 |
| ~1024 tokens | 1234.2 |
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("sahilchachra/supra-50m-instruct-fp16-mlx")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)
| Model | Variant |
|---|---|
| sahilchachra/supra-50m-instruct-8bit-mlx | Affine int8 |
| sahilchachra/supra-50m-instruct-optiq-5bpw-mlx | OptiQ mixed-precision (target 5.0 bpw) |
See SupraLabs/Supra-50M-Instruct for full model details and intended use.
Quantized