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 evaluation/per-question/mlx-8bit.jsonl from apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit: direct link, hf CLI and curl.
- Browser
- Download file 27.9 kB
-
https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/resolve/main/evaluation/per-question/mlx-8bit.jsonl
- Command line
-
hf download hf://apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/evaluation/per-question/mlx-8bit.jsonl
-
curl -L -o mlx-8bit.jsonl https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit/resolve/main/evaluation/per-question/mlx-8bit.jsonl
27.9 kB
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| {"panel_index": 18, "task_family": "hs3", "logits": [19.625, 16.5], "probabilities": [0.957912266254425, 0.04208773002028465], "choice": "yes", "correct": true, "agrees_with_reference": true, "max_abs_probability_delta": 0.004760846495628357} | |
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| {"panel_index": 23, "task_family": "hs3", "logits": [19.0, 15.4375], "probabilities": [0.9724147915840149, 0.027585279196500778], "choice": "yes", "correct": false, "agrees_with_reference": true, "max_abs_probability_delta": 0.0016279220581054688} | |
| {"panel_index": 24, "task_family": "hs3", "logits": [19.0, 12.875], "probabilities": [0.9978172779083252, 0.0021827167365700006], "choice": "yes", "correct": true, "agrees_with_reference": true, "max_abs_probability_delta": 2.3283064365386963e-10} | |
| {"panel_index": 25, "task_family": "hs3", "logits": [13.125, 19.0], "probabilities": [0.0028009277302771807, 0.9971990585327148], "choice": "no", "correct": true, "agrees_with_reference": true, "max_abs_probability_delta": 6.984919309616089e-10} | |
| {"panel_index": 26, "task_family": "hs3", "logits": [18.875, 16.125], "probabilities": [0.9399133324623108, 0.06008664891123772], "choice": "yes", "correct": false, "agrees_with_reference": true, "max_abs_probability_delta": 0.006683349609375} | |
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