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-v1-9B-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-v1-9B-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-v1-9B-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-v1-9B-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-v1-9B-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-v1-9B-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use apus-ailab/APUS-OpenJev-v1-9B-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-v1-9B-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-v1-9B-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-v1-9B-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use apus-ailab/APUS-OpenJev-v1-9B-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-v1-9B-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-v1-9B-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use apus-ailab/APUS-OpenJev-v1-9B-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-v1-9B-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-v1-9B-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 openjev_contracts.py from apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit: direct link, hf CLI and curl.
- Browser
- Download file 3.85 kB
-
https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit/resolve/main/openjev_contracts.py
- Command line
-
hf download hf://apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit/openjev_contracts.py
-
curl -L -o openjev_contracts.py https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit/resolve/main/openjev_contracts.py
3.85 kB
| """Small shared contract. Prompts use a strict whitelist of input fields.""" | |
| import json | |
| import math | |
| PROMPT_VERSION = "jev.dynamic.prompt.v2" | |
| LABELS = tuple("ABCDEFGHIJKLMNOP") | |
| BINARY_CRITERIA = [ | |
| {"id": "yes", "description": "The stated proposition is true."}, | |
| {"id": "no", "description": "The stated proposition is false."}, | |
| ] | |
| def validate_request(record): | |
| for key in ("id", "group_id", "state", "instructions"): | |
| if not isinstance(record.get(key), str) or not record[key].strip(): | |
| raise ValueError(f"{key} must be a nonempty string") | |
| if record.get("primitive") not in ("choice", "noul", "score_level"): | |
| raise ValueError("unsupported primitive") | |
| criteria = record.get("criteria") | |
| if not isinstance(criteria, list) or not 2 <= len(criteria) <= len(LABELS): | |
| raise ValueError("criteria must contain 2..16 candidates") | |
| ids = [] | |
| for candidate in criteria: | |
| if not isinstance(candidate, dict): | |
| raise TypeError("candidate must be an object") | |
| for key in ("id", "description"): | |
| if not isinstance(candidate.get(key), str) or not candidate[key].strip(): | |
| raise ValueError(f"candidate {key} must be nonempty") | |
| ids.append(candidate["id"]) | |
| if len(set(ids)) != len(ids): | |
| raise ValueError("duplicate candidate ids") | |
| if record["primitive"] != "choice" and criteria != BINARY_CRITERIA: | |
| raise ValueError("noul and score_level require canonical yes/no criteria") | |
| def validate_record(record): | |
| validate_request(record) | |
| if record.get("gold") not in [c["id"] for c in record["criteria"]]: | |
| raise ValueError("gold must be a candidate id") | |
| if not isinstance(record.get("provenance"), dict): | |
| raise TypeError("provenance must be an object") | |
| def label_mapping(record): | |
| validate_request(record) | |
| return dict(zip(LABELS, (c["id"] for c in record["criteria"]))) | |
| def render_prompt_parts(record): | |
| """Text prefix/suffix; callers MUST check tokenizer boundary equivalence.""" | |
| validate_request(record) | |
| prefix = "Shared state:\n" + record["state"] + "\n\n" | |
| task = { | |
| "primitive": record["primitive"], | |
| "instructions": record["instructions"], | |
| "criteria": [ | |
| {"label": label, "description": candidate["description"]} | |
| for label, candidate in zip(LABELS, record["criteria"]) | |
| ], | |
| } | |
| suffix = json.dumps(task, ensure_ascii=False, sort_keys=True) | |
| suffix += ( | |
| "\nReturn only the selected letter: " | |
| + ", ".join(LABELS[: len(record["criteria"])]) | |
| + ".\nAnswer:" | |
| ) | |
| return prefix, suffix | |
| def render_prompt(record): | |
| return "".join(render_prompt_parts(record)) | |
| def to_messages(record): | |
| validate_record(record) | |
| inverse = {candidate: label for label, candidate in label_mapping(record).items()} | |
| return { | |
| "messages": [ | |
| {"role": "user", "content": render_prompt(record)}, | |
| {"role": "assistant", "content": inverse[record["gold"]]}, | |
| ] | |
| } | |
| def format_response(record, probabilities): | |
| """Map ordered candidate probabilities; score_level is NOT aggregate Score.""" | |
| mapping = label_mapping(record) | |
| values = list(probabilities) | |
| if len(values) != len(mapping) or any( | |
| not math.isfinite(p) or p < 0 or p > 1 for p in values | |
| ): | |
| raise ValueError("invalid probabilities") | |
| if not math.isclose(sum(values), 1, abs_tol=1e-5): | |
| raise ValueError("probabilities must sum to one") | |
| distribution = dict(zip(mapping.values(), values)) | |
| result = {"type": record["primitive"], "probabilities": distribution} | |
| if record["primitive"] == "choice": | |
| result["choice"] = max(distribution, key=distribution.get) | |
| else: | |
| result["yes_probability"] = distribution["yes"] | |
| return result | |