Image-Text-to-Text
MLX
Safetensors
English
qwen3_5
text-generation-inference
apple-silicon
4-bit precision
8-bit precision
mlx-vlm
conversational
Instructions to use prithivMLmods/Zenith-9B-CodeCore-Merge-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use prithivMLmods/Zenith-9B-CodeCore-Merge-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("prithivMLmods/Zenith-9B-CodeCore-Merge-MLX") config = load_config("prithivMLmods/Zenith-9B-CodeCore-Merge-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use prithivMLmods/Zenith-9B-CodeCore-Merge-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 "prithivMLmods/Zenith-9B-CodeCore-Merge-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": "prithivMLmods/Zenith-9B-CodeCore-Merge-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use prithivMLmods/Zenith-9B-CodeCore-Merge-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 "prithivMLmods/Zenith-9B-CodeCore-Merge-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 prithivMLmods/Zenith-9B-CodeCore-Merge-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Zenith-9B-CodeCore-Merge-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 "prithivMLmods/Zenith-9B-CodeCore-Merge-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 "prithivMLmods/Zenith-9B-CodeCore-Merge-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"
| base_model_relation: quantized | |
| language: en | |
| library_name: mlx | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - mlx | |
| - text-generation-inference | |
| - apple-silicon | |
| - 4-bit | |
| - 8-bit | |
| - qwen3_5 | |
| - mlx-vlm | |
| base_model: | |
| - prithivMLmods/Zenith-9B-CodeCore-Merge | |
| license: apache-2.0 | |
| # **Zenith-9B-CodeCore-Merge-MLX** | |
| > Zenith-9B-CodeCore-Merge is a merged 9B-parameter coding and reasoning model designed for long-horizon coding tasks, agentic coding, and agentic reasoning. It is built by merging [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) as the base model with [OxCoder-9B](https://huggingface.co/OrionLLM/OxCoder-9B), [Qwopus3.5-9B-Coder](https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder), and [Ornith-1.5-9B](https://huggingface.co/ornith-ai/Ornith-1.5-9B), combining their capabilities for code generation, multi-step problem solving, instruction following, and autonomous coding workflows. The model is intended for complex software-engineering tasks that require sustained reasoning across multiple steps, code understanding, modification, debugging, and tool-oriented agentic workflows. This model is experimental and may generate artifacts. | |
| - *GGUF: [Zenith-9B-CodeCore-Merge-GGUF](https://huggingface.co/prithivMLmods/Zenith-9B-CodeCore-Merge-GGUF). Note: The Multi-Token Prediction (MTP) heads are not preserved in this format. The model runs as a standard single-token-per-step autoregressive decoder.* | |
| ## Repository layout | |
| ```plaintext | |
| +-- prithivMLmods/Zenith-9B-CodeCore-Merge-MLX (main) | |
| +-- / (Root: bf16) | |
| +-- 4bit/ (Quantized: 4-bit) | |
| +-- 8bit/ (Quantized: 8-bit) | |
| ``` | |
| ## Use with mlx | |
| Install the required library: | |
| ```bash | |
| pip install -U mlx-vlm | |
| ``` | |
| > **Model Note:** `Zenith-9B-CodeCore-Merge` is a 9-billion parameter multimodal coding model designed for code generation, visual debugging, repository reasoning, and architecture diagram analysis. It supports both text and image/screenshot inputs. | |
| ### BF16 Variant (Base Weights) | |
| The full-precision BF16 files reside directly in the root of the repository: | |
| #### CLI (Terminal) | |
| ```bash | |
| python -m mlx_vlm generate \ | |
| --model prithivMLmods/Zenith-9B-CodeCore-Merge-MLX \ | |
| --max-tokens 512 \ | |
| --temperature 0.0 \ | |
| --prompt "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." \ | |
| --image <path_to_image> | |
| ``` | |
| #### Python API | |
| ```python | |
| from mlx_vlm import load, generate | |
| from mlx_vlm.prompt_utils import apply_chat_template | |
| from mlx_vlm.utils import load_config | |
| model_path = "prithivMLmods/Zenith-9B-CodeCore-Merge-MLX" | |
| model, processor = load(model_path) | |
| config = load_config(model_path) | |
| image = ["<path_to_image>"] | |
| prompt = "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." | |
| formatted_prompt = apply_chat_template(processor, config, prompt, num_images=len(image)) | |
| output = generate( | |
| model, | |
| processor, | |
| formatted_prompt, | |
| image=image, | |
| max_tokens=512, | |
| temperature=0.0 | |
| ) | |
| print(output.text) | |
| ``` | |
| ### 8-bit Variant | |
| Target the `8bit` subfolder: | |
| #### CLI (Terminal) | |
| ```bash | |
| python -m mlx_vlm generate \ | |
| --model prithivMLmods/Zenith-9B-CodeCore-Merge-MLX/8bit \ | |
| --max-tokens 512 \ | |
| --temperature 0.0 \ | |
| --prompt "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." \ | |
| --image <path_to_image> | |
| ``` | |
| #### Python API | |
| ```python | |
| from mlx_vlm import load, generate | |
| from mlx_vlm.prompt_utils import apply_chat_template | |
| from mlx_vlm.utils import load_config | |
| model_path = "prithivMLmods/Zenith-9B-CodeCore-Merge-MLX" | |
| model, processor = load(model_path, subfolder="8bit") | |
| config = load_config(model_path, subfolder="8bit") | |
| image = ["<path_to_image>"] | |
| prompt = "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." | |
| formatted_prompt = apply_chat_template(processor, config, prompt, num_images=len(image)) | |
| output = generate( | |
| model, | |
| processor, | |
| formatted_prompt, | |
| image=image, | |
| max_tokens=512, | |
| temperature=0.0 | |
| ) | |
| print(output.text) | |
| ``` | |
| ### 4-bit Variant | |
| Target the `4bit` subfolder: | |
| #### CLI (Terminal) | |
| ```bash | |
| python -m mlx_vlm generate \ | |
| --model prithivMLmods/Zenith-9B-CodeCore-Merge-MLX/4bit \ | |
| --max-tokens 512 \ | |
| --temperature 0.0 \ | |
| --prompt "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." \ | |
| --image <path_to_image> | |
| ``` | |
| #### Python API | |
| ```python | |
| from mlx_vlm import load, generate | |
| from mlx_vlm.prompt_utils import apply_chat_template | |
| from mlx_vlm.utils import load_config | |
| model_path = "prithivMLmods/Zenith-9B-CodeCore-Merge-MLX" | |
| model, processor = load(model_path, subfolder="4bit") | |
| config = load_config(model_path, subfolder="4bit") | |
| image = ["<path_to_image>"] | |
| prompt = "Analyze this code screenshot, explain what it does, and fix any potential bugs or edge cases." | |
| formatted_prompt = apply_chat_template(processor, config, prompt, num_images=len(image)) | |
| output = generate( | |
| model, | |
| processor, | |
| formatted_prompt, | |
| image=image, | |
| max_tokens=512, | |
| temperature=0.0 | |
| ) | |
| print(output.text) | |
| ``` | |
| ## License and Attribution | |
| This model is based on and/or incorporates the following open-source projects and models: | |
| * **Qwen3.5-9B (Base):** [https://huggingface.co/Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | |
| * **Zenith-9B-CodeCore-Merge:** [https://huggingface.co/prithivMLmods/Zenith-9B-CodeCore-Merge](https://huggingface.co/prithivMLmods/Zenith-9B-CodeCore-Merge) | |
| * **mlx-vlm:** [https://github.com/Blaizzy/mlx-vlm](https://github.com/Blaizzy/mlx-vlm) | |
| * **MLX:** [https://github.com/ml-explore/mlx](https://github.com/ml-explore/mlx) | |
| This model is released under the [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). |