Instructions to use prithivMLmods/Zenith-9B-CodeCore-Merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Zenith-9B-CodeCore-Merge with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Zenith-9B-CodeCore-Merge")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Zenith-9B-CodeCore-Merge")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Zenith-9B-CodeCore-Merge", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Zenith-9B-CodeCore-Merge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Zenith-9B-CodeCore-Merge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Zenith-9B-CodeCore-Merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Zenith-9B-CodeCore-Merge

SGLang

How to use prithivMLmods/Zenith-9B-CodeCore-Merge with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Zenith-9B-CodeCore-Merge" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Zenith-9B-CodeCore-Merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Zenith-9B-CodeCore-Merge" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Zenith-9B-CodeCore-Merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Zenith-9B-CodeCore-Merge with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Zenith-9B-CodeCore-Merge
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Zenith-9B-CodeCore-Merge

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 as the base model with OxCoder-9B, Qwopus3.5-9B-Coder, and 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. 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.

Merge Recipe

hf download OrionLLM/OxCoder-9B \
  --local-dir ./models/OxCoder-9B

hf download Jackrong/Qwopus3.5-9B-Coder \
  --local-dir ./models/Qwopus3.5-9B-Coder

hf download ornith-ai/Ornith-1.5-9B \
  --local-dir ./models/Ornith-1.5-9B

hf download Qwen/Qwen3.5-9B \
  --local-dir ./models/Qwen3.5-9B

python omnimergekit.py \
  --base      models/Qwen3.5-9B --task-base ./models/Qwen3.5-9B \
  --source    models/OxCoder-9B \
  --source    models/Qwopus3.5-9B-Coder \
  --source    models/Ornith-1.5-9B \
  --weights   0.55,0.30,0.15 \
  --method omnimerge_v2 --density 0.53 --darex-q 0.75 --seed 42 \
  --no-auto-mlp-skip --skip-patterns visual.,mtp. \
  --output prithivMLmods/Zenith-9B-CodeCore-Merge