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"
File size: 5,799 Bytes
20d2ee4 538a672 20d2ee4 538a672 5ce3ad2 538a672 20d2ee4 daee27d 90efbb7 166348c 38a0ff9 daee27d 90efbb7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | ---
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). |