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

Libraries

How to use ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX 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("ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX")

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 ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX with Pi:

Start the MLX server

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

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX with MLX LM:

Generate or start a chat session

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

Run an OpenAI-compatible server

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

Hermes Agent

How to use ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX 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 "ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX"

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 ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX with OpenClaw:

Start the MLX server

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

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 "ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX" \
  --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"

FastContext-1.0-4B-SFT MLX Dynamic 4-to-6-bit

MLX conversion of microsoft/FastContext-1.0-4B-SFT, quantized with mlx_lm.dynamic_quant.

Quantization

Method: MLX-LM dynamic mixed precision
Target: 5.0 bits per weight
Actual: 4.984 bits per weight
Low precision: 4-bit, group size 64
High precision: 6-bit, group size 64
Source revision: 80b60c036a612354e7c611cdabc005ec67f76993

Local Evaluation

Short-context evaluation command:

mlx_lm.perplexity \
  --model ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX \
  --num-samples 64 \
  --sequence-length 512

Longer-context evaluation command:

mlx_lm.perplexity \
  --model ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX \
  --num-samples 32 \
  --sequence-length 2048

Results on allenai/tulu-3-sft-mixture:

Model	Seq len	Samples	Size	Peak memory	Perplexity
BF16 MLX	512	64	7.5G	12.34 GB	7.732 +/- 0.121
Dynamic 4-to-6-bit	512	64	2.3G	6.80 GB	7.956 +/- 0.125
BF16 MLX	2048	32	7.5G	24.28 GB	4.258 +/- 0.041
Dynamic 4-to-6-bit	2048	32	2.3G	18.74 GB	4.349 +/- 0.042

Quantization run reported calibration PPL 8.025 -> 8.300 and peak quantization memory 38.217GB.

Usage

mlx_lm.generate \
  --model ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX \
  --prompt "Explain what repository exploration means." \
  --max-tokens 128 \
  --temp 0.0

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Safetensors

Model size

4B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for ukint-vs/FastContext-1.0-4B-SFT-Dynamic-4bit-MLX

Base model

microsoft/FastContext-1.0-4B-SFT

Quantized

(29)

this model