Instructions to use chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chanderbalaji/Intern-S2-Preview-FP8-MLX-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("chanderbalaji/Intern-S2-Preview-FP8-MLX-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
LM Studio

Pi new

How to use chanderbalaji/Intern-S2-Preview-FP8-MLX-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 "chanderbalaji/Intern-S2-Preview-FP8-MLX-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": "chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use chanderbalaji/Intern-S2-Preview-FP8-MLX-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 "chanderbalaji/Intern-S2-Preview-FP8-MLX-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 chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit

Run Hermes

hermes

MLX LM

How to use chanderbalaji/Intern-S2-Preview-FP8-MLX-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 "chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "chanderbalaji/Intern-S2-Preview-FP8-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Intern-S2-Preview-FP8-MLX-4bit / README.md

chanderbalaji

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metadata

language:
  - en
base_model: internlm/Intern-S2-Preview
tags:
  - mlx
  - fp8
  - 4bit
  - intern-s2-preview
  - apple-silicon
  - mlx-lm
pipeline_tag: text-generation
library_name: mlx

Intern-S2-Preview FP8 MLX 4-bit

This repository contains an MLX-compatible 4-bit version of internlm/Intern-S2-Preview.

Local Usage

python -m mlx_lm generate \
  --model <namespace>/Intern-S2-Preview-FP8-MLX-4bit \
  --trust-remote-code \
  --prompt "Write a concise response to your prompt here." \
  --max-tokens 4096

For a local checkout:

python -m mlx_lm generate \
  --model /path/to/Intern-S2-Preview-FP8-MLX-4bit \
  --trust-remote-code \
  --prompt "Write a concise response to your prompt here." \
  --max-tokens 4096

Local Benchmark

Benchmarks were run locally with mlx_lm generate on Apple Silicon.

Basic Generation

Command:

python -m mlx_lm generate \
  --model /path/to/Intern-S2-Preview-FP8-MLX-4bit \
  --trust-remote-code \
  --prompt "Write a concise response to your prompt here." \
  --max-tokens 4096

Observed output stats:

Metric	Value
Prompt tokens	19
Prompt throughput	306.835 tokens/sec
Generation tokens	702
Generation throughput	123.388 tokens/sec
Peak memory	19.651 GB

Prompted Final-Only Output Test

Command:

python -m mlx_lm generate \
  --model /path/to/Intern-S2-Preview-FP8-MLX-4bit \
  --trust-remote-code \
  --prompt "Do not show reasoning, analysis, thinking process, scratchpad, or <think> text. Output only the final answer. Write a concise response to your prompt here." \
  --max-tokens 4096

Observed output stats:

Metric	Value
Prompt tokens	44
Prompt throughput	487.095 tokens/sec
Generation tokens	817
Generation throughput	122.650 tokens/sec
Peak memory	19.695 GB

The model still emitted visible reasoning text in this raw generation mode, so prompt-only suppression was not sufficient.

Notes

Format: MLX sharded safetensors
Quantization: FP8/4-bit MLX local build
Base model: internlm/Intern-S2-Preview
The model may emit visible reasoning text in raw generation. For chat applications, use a serving layer or post-processor that strips reasoning if needed.
Raw generation throughput was about 123 tokens/sec in the local smoke tests above.
Peak memory in these tests was about 19.7 GB.

License

This is a derived MLX build of internlm/Intern-S2-Preview. Refer to the base model repository for upstream license and usage terms.