Instructions to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("rubybear/FastContext-1.0-4B-SFT-mlx-8bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rubybear/FastContext-1.0-4B-SFT-mlx-8bit

Run Hermes

hermes

OpenClaw new

How to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "rubybear/FastContext-1.0-4B-SFT-mlx-8bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use rubybear/FastContext-1.0-4B-SFT-mlx-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rubybear/FastContext-1.0-4B-SFT-mlx-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "rubybear/FastContext-1.0-4B-SFT-mlx-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

FastContext-1.0-4B-SFT-mlx-8bit / README.md

rubybear

Upload 8-bit quantization of FastContext-1.0-4B-SFT

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metadata

language:
  - en
license: mit
tags:
  - Explorer SubAgent
  - Repository Exploration
  - mlx
library_name: mlx
base_model: microsoft/FastContext-1.0-4B-SFT
pipeline_tag: text-generation

FastContext-1.0-4B-SFT-mlx-8bit

8-bit MLX quantization of microsoft/FastContext-1.0-4B-SFT for Apple Silicon.

Quantization details

Method: Affine 8-bit
Group size: 64
Effective bits per weight: 8.5
Model size: 4.0 GB (vs 7.5 GB bf16)

Benchmark results

Tested on 10 SWE-bench Multilingual instances against other quantization variants:

Model	Bits/Wt	Size	File F1	Line F1
affine 8-bit g64 (this model)	8.5	4.0G	0.507	0.140
affine 4-bit g32	5.0	2.4G	0.300	0.090
affine 3-bit g64	3.5	1.7G	0.100	0.000
affine 4-bit g64	4.5	2.1G	0.050	0.005
mattrobenolt 4-bit g64	4.5	2.1G	0.025	0.008

Highest quality quantization — best File F1 and Line F1 at the cost of larger size and slower inference.

Usage

from mlx_lm import load, generate

model, tokenizer = load("rubybear/FastContext-1.0-4B-SFT-mlx-8bit")

Or with fastcontext-mcp for Claude Code integration.