Text Generation
MLX
Safetensors
English
intern_s2_preview
fp8
4bit
intern-s2-preview
apple-silicon
mlx-lm
conversational
custom_code
4-bit precision
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
- MLX
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"} ] }'
File size: 2,621 Bytes
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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.
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