Text Generation
MLX
Safetensors
English
k2_horizon
mlx-lm
4-bit precision
k2-horizon
long-context
512k-context
dense
conversational
custom_code
Instructions to use abenzerps/K2-Horizon-3.7B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use abenzerps/K2-Horizon-3.7B-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("abenzerps/K2-Horizon-3.7B-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 Settings
- LM Studio
- Pi
How to use abenzerps/K2-Horizon-3.7B-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 "abenzerps/K2-Horizon-3.7B-MLX-4bit"
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": "abenzerps/K2-Horizon-3.7B-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use abenzerps/K2-Horizon-3.7B-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 "abenzerps/K2-Horizon-3.7B-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "abenzerps/K2-Horizon-3.7B-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": "abenzerps/K2-Horizon-3.7B-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use abenzerps/K2-Horizon-3.7B-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 "abenzerps/K2-Horizon-3.7B-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 abenzerps/K2-Horizon-3.7B-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use abenzerps/K2-Horizon-3.7B-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "abenzerps/K2-Horizon-3.7B-MLX-4bit"
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 "abenzerps/K2-Horizon-3.7B-MLX-4bit" \ --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"
| base_model: IFM/K2-Horizon-3.7B | |
| base_model_relation: quantized | |
| license: apache-2.0 | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| library_name: mlx | |
| tags: | |
| - mlx | |
| - mlx-lm | |
| - 4-bit | |
| - k2-horizon | |
| - long-context | |
| - 512k-context | |
| - dense | |
| # K2-Horizon-3.7B MLX — 4-bit | |
| MLX 4-bit conversion of [IFM/K2-Horizon-3.7B](https://huggingface.co/IFM/K2-Horizon-3.7B), a 3.7B dense decoder-only model for reasoning, coding, long-context work, and tool use. The source checkpoint supports a native context length of **524,288 tokens (512K)**. | |
| ## Benchmarks | |
|  | |
| *Benchmark results reported by IFM for the original IFM/K2-Horizon-3.7B checkpoint.* | |
| ## Release | |
| | Format | Quantization | Size | | |
| | --- | --- | ---: | | |
| | MLX safetensors | Affine 4-bit, group size 64 | 2.87 GB | | |
| The included `k2_horizon_mlx.py` adapter preserves K2 Horizon's grouped RMSNorm. Use it with MLX-LM and `--trust-remote-code`. The model is text-only; no vision projector or MTP files are included. | |
| ## Usage | |
| ```bash | |
| pip install -U mlx-lm | |
| mlx_lm.generate \ | |
| --model . \ | |
| --trust-remote-code \ | |
| --prompt "Explain why reproducible builds matter." \ | |
| --max-tokens 512 | |
| ``` | |
| ## Source | |
| - Model: [IFM/K2-Horizon-3.7B](https://huggingface.co/IFM/K2-Horizon-3.7B) | |
| - Source revision: [`633f52a`](https://huggingface.co/IFM/K2-Horizon-3.7B/tree/633f52ad28b17edeabd82afc61d2d13b4c59a561) | |
| - License: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | |
| - Checksums: [SHA256SUMS.txt](SHA256SUMS.txt) | |