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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	---
	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`](https://huggingface.co/internlm/Intern-S2-Preview).

	## Local Usage

	```bash
	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:

	```bash
	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:

	```bash
	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:

	```bash
	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.