Text Generation
MLX
Safetensors
interns2_mobius
mlx-lm
quantization
4-bit precision
conversational
custom_code
Instructions to use nightscape/Intern-S2-Mobius-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nightscape/Intern-S2-Mobius-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("nightscape/Intern-S2-Mobius-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
- Pi
How to use nightscape/Intern-S2-Mobius-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 "nightscape/Intern-S2-Mobius-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": "nightscape/Intern-S2-Mobius-4bit-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use nightscape/Intern-S2-Mobius-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 "nightscape/Intern-S2-Mobius-4bit-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "nightscape/Intern-S2-Mobius-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": "nightscape/Intern-S2-Mobius-4bit-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use nightscape/Intern-S2-Mobius-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 "nightscape/Intern-S2-Mobius-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 nightscape/Intern-S2-Mobius-4bit-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nightscape/Intern-S2-Mobius-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 "nightscape/Intern-S2-Mobius-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 "nightscape/Intern-S2-Mobius-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"
File size: 2,600 Bytes
b23eaff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | ---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/internlm/Intern-S2-Mobius/blob/main/LICENSE
pipeline_tag: text-generation
base_model: internlm/Intern-S2-Mobius
tags:
- mlx
- mlx-lm
- quantization
- 4-bit
---
# Intern-S2-Mobius (4-bit MLX)
An MLX **4-bit** (affine, group size 64) quantization of
[internlm/Intern-S2-Mobius](https://huggingface.co/internlm/Intern-S2-Mobius) — a 35B hybrid model:
Gated-DeltaNet linear attention / full attention at interval 4, with 2560 experts in 4 globally-shared
routed MoE banks. Runs on Apple Silicon.
- Base model: [internlm/Intern-S2-Mobius](https://huggingface.co/internlm/Intern-S2-Mobius) (Apache-2.0).
- Quantization: 4-bit affine, group size 64; shared-expert / router gates kept at 8-bit. ~19.6 GB peak, ~4.5 bpw.
- Architecture: `interns2_mobius` (`text_config.model_type = interns2_mobius_text`), 40 layers, `head_dim` 256,
MoE 2560 experts / top-8, `num_blocks` 4. `max_position_embeddings` 262144.
## Quick start
Requires the `mlx-lm` build that ships the `interns2_mobius` architecture
(official part of `mlx-lm` as of the model-support PR):
```bash
pip install -U mlx-lm
mlx_lm.generate --model nightscape/Intern-S2-Mobius-4bit-mlx \
-p "The secret to baking a good cake is" -m 1024 --trust-remote-code
```
`--trust-remote-code` is mandatory: the checkpoint bundles a custom tokenizer
(`tokenization_interns1.py`) and model code.
## Details
- This is an MLX conversion; `transformers` does not yet ship `interns2_mobius`. The reference is the
upstream repo's `trust_remote_code` implementation, verified by full bf16 logit diff (argmax agreement
38/39, the sole miss a bit-identical tie).
- Capability spot-check (this 4-bit conversion): MMLU-Pro 88.3% ± 4.1 (n=60) vs upstream bf16 89.05;
GSM8K 97–98% (n=100). Short-generation-budget evals truncate chain-of-thought and depress scores — an
eval artifact, not a capability drop.
- Text-only: this conversion is the language model. The upstream checkpoint is tagged `image-text-to-text`,
but the MLX port loads the language model and generates text (no vision tower on this path).
## Companion
An experimental MTP (Multi-Token-Prediction) head is published separately:
[nightscape/Intern-S2-Mobius-4bit-mlx-mtp](https://huggingface.co/nightscape/Intern-S2-Mobius-4bit-mlx-mtp).
It is consumed by the `omlx` server's `interns2_mobius` MTP driver, **not** by stock `mlx-lm`.
## License
Weights and code under Apache-2.0 — see [LICENSE](LICENSE). Model by InternLM; this is a derivative
conversion of their weights plus the MLX port.
|