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

Libraries

How to use mlx-community/FastContext-1.0-4B-SFT-4bit 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("mlx-community/FastContext-1.0-4B-SFT-4bit")

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 mlx-community/FastContext-1.0-4B-SFT-4bit with Pi:

Start the MLX server

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

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": "mlx-community/FastContext-1.0-4B-SFT-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the MLX server

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

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 "mlx-community/FastContext-1.0-4B-SFT-4bit" \
  --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 mlx-community/FastContext-1.0-4B-SFT-4bit with MLX LM:

Generate or start a chat session

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

Run an OpenAI-compatible server

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

Hermes Agent

How to use mlx-community/FastContext-1.0-4B-SFT-4bit 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 "mlx-community/FastContext-1.0-4B-SFT-4bit"

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 mlx-community/FastContext-1.0-4B-SFT-4bit

Run Hermes

hermes

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

manjunathshiva

Add FastContext-1.0-4B-SFT MLX 4-bit quant

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metadata

language: en
license: mit
library_name: mlx
base_model: ShaunGves/FastContext-1.0-4B-SFT
tags:
  - mlx
  - code
  - agent
  - repository-exploration
pipeline_tag: text-generation

FastContext-1.0-4B-SFT — MLX 4-bit

MLX 4-bit quantization (4.501 bits per weight, ~2.1 GB) of ShaunGves/FastContext-1.0-4B-SFT, a surviving mirror of Microsoft's FastContext repository-exploration subagent (arXiv:2606.14066), removed from the official listings on 2026-06-30. Base model: Qwen3-4B-Instruct-2507. Converted with mlx-lm 0.29.1 (default group size 64).

FastContext is a small explorer model for coding agents: given a natural-language query about a repository, it explores with read-only tools (Read / Glob / Grep, called in parallel) and returns a compact <final_answer> block of file:line-range citations, keeping broad exploration out of the main agent's context window.

⚠️ Quality note (tested)

In end-to-end tests with the fastcontext CLI on an M4 Mac, this 4-bit quant showed noticeably degraded path grounding: it repeatedly assumed wrong repository root paths, failed to recover from tool errors, and hallucinated citations to nonexistent files — on the same queries the 8-bit quant answered accurately. It is published for memory-constrained setups and further experimentation; prefer the 8-bit quant (~4 GB) if your machine allows. Run with temperature ≤ 0.6 and cap max_tokens (missed stop tokens otherwise generate for minutes).

Use with LM Studio

Search for FastContext-1.0-4B-SFT-4bit in LM Studio (MLX runtime), or:

lms get mlx-community/FastContext-1.0-4B-SFT-4bit

Use with mlx-lm

uv tool install "mlx-lm==0.29.1" --with "transformers<5" --with "mlx<0.31"
mlx_lm.server --model mlx-community/FastContext-1.0-4B-SFT-4bit --port 8080

Use with the fastcontext CLI

Install from the preserved mirror (includes fixes for local OpenAI-compatible servers), then from the repo you want to explore:

export BASE_URL="http://localhost:8080/v1"   # or http://localhost:1234/v1 for LM Studio
export MODEL="mlx-community/FastContext-1.0-4B-SFT-4bit"
export API_KEY="local"
export TEMPERATURE=0.6
export MAX_TOKENS=4000

fastcontext -q "Where is the retry logic for failed API calls?" --citation