Text Generation
MLX
Safetensors
English
Chinese
qwen3_5
apus-openjev
decision-model
apple-silicon
conversational
8-bit precision
Instructions to use apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit 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("apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit") 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 apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit"
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": "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit 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 "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit"
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 apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit"
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 "apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit" \ --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"
|
Download README.zh-CN.md from apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit: direct link, hf CLI and curl.
- Browser
- Download file 2.85 kB
-
https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/resolve/main/README.zh-CN.md
- Command line
-
hf download hf://apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/README.zh-CN.md
-
curl -L -o README.zh-CN.md https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/resolve/main/README.zh-CN.md
2.85 kB
| library_name: mlx | |
| license: apache-2.0 | |
| base_model: apus-ailab/APUS-OpenJev-v1-4B | |
| base_model_relation: quantized | |
| pipeline_tag: text-generation | |
| language: | |
| - en | |
| - zh | |
| tags: | |
| - apus-openjev | |
| - decision-model | |
| - mlx | |
| - apple-silicon | |
| # APUS-OpenJev-v1-4B-MLX-8bit | |
| [English](README.md) | [中文](README.zh-CN.md) · [源模型](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) · [Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825) · [GGUF collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-gguf-6ab39d5e724c4d8a1021198f) · [MLX collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-mlx-6ab39d5fc988a1b1cb89dfc8) · [MLX-4bit](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-MLX-4bit) · [GGUF / Ollama](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-GGUF) | |
| [APUS-OpenJev-v1-4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) 的 MLX 权重(8bit affine,group size 64),适用于 **Apple Silicon Mac**(mlx-lm、LM Studio)。 | |
| OpenJev 是**决策模型**:每个请求给出状态、指令和 2–16 个候选,模型对候选标签 A–P 打分。它不是聊天模型。 | |
| ## 快速开始 | |
| ```bash | |
| pip install mlx-lm | |
| hf download apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit --local-dir ./openjev | |
| python ./openjev/examples/openjev_mlx.py --model ./openjev | |
| ``` | |
| `examples/openjev_mlx.py` 使用 [openjev_contracts.py](openjev_contracts.py)(训练时的格式)渲染 prompt,返回精确的候选分布。 | |
| ## 一致性 | |
| Frozen80 使用完全相同的 prompt token,与 HF BF16 发布版(完整深度,**66/80 · 82.50%**)对比: | |
| | Run / 运行 | Backend / 后端 | Frozen80 | = HF BF16 | Max Δp | | |
| |---|---|---:|---:|---:| | |
| | NVIDIA RTX PRO 6000 (CUDA) | mlx 0.32.2 on Linux x86_64 (Device(gpu, 0)) | 66/80 · 82.50% | 80/80 | 0.1201 | | |
| | Apple M5 24 GB (Metal) | mlx 0.32.2 on Darwin arm64 (Device(gpu, 0)) | 66/80 · 82.50% | 80/80 | 0.1480 | | |
| 已在 Apple M5, 24 GB 上实测:峰值内存 5.44 GB。同一份文件在 CUDA 与 Metal 上 80/80 题决策完全相同(最大 Δp 0.054)。Frozen80 是复用的开发面板,不是盲测。逐题明细(候选概率、选择、是否正确,可按 `panel_index` 与 Frozen80 关联):[evaluation/per-question/](evaluation/per-question/)。 | |
| ## 转换说明 | |
| - mlx-lm `0.31.3` / mlx `0.32.2`;8bit affine,group size 64。 | |
| - GDN 的 `A_log` 和 `linear_attn.norm.weight` 保持源精度(35B 发布版中为 FP32)。 | |
| - 仅完整深度、仅文本,概率**未经校准**。 | |
| ## 许可 | |
| Apache-2.0,继承自源模型,见 [LICENSE](LICENSE)。基座模型:[Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B)。 | |
| **作者:** gumpcheng([xDAN2099](https://huggingface.co/xDAN2099))、zhangxu、[APUS AI-LAB](https://github.com/APUS-AI-Lab)。 | |