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

Libraries

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

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

pipe = pipeline("text-generation", model="prithivMLmods/Vortex-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/Vortex-9B-CodeCore-Merge")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Vortex-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/Vortex-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/Vortex-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/Vortex-9B-CodeCore-Merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use prithivMLmods/Vortex-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/Vortex-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/Vortex-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/Vortex-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/Vortex-9B-CodeCore-Merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Vortex-9B-CodeCore-Merge with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Vortex-9B-CodeCore-Merge
```

prithivMLmods commited on 17 days ago

Commit

4ddbc29

verified ·

1 Parent(s): a4dbf14

Update README.md

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README.md +39 -1

README.md CHANGED Viewed

@@ -22,4 +22,42 @@ language:
 - en
 pipeline_tag: text-generation
 library_name: transformers
----

 - en
 pipeline_tag: text-generation
 library_name: transformers
+---
+# **Vortex-9B-CodeCore-Merge**
+> Vortex-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 [NeoHorse-1-9B](https://huggingface.co/TokenRhythm/NeoHorse-1-9B), [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: [Vortex-9B-CodeCore-Merge-GGUF](https://huggingface.co/prithivMLmods/Vortex-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
+```bash
+hf download TokenRhythm/NeoHorse-1-9B \
+  --local-dir ./models/NeoHorse-1-9B
+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
+```
+```bash
+python omnimergekit.py \
+  --base      models/Qwen3.5-9B --task-base ./models/Qwen3.5-9B \
+  --source    models/NeoHorse-1-9B \
+  --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/Vortex-9B-CodeCore-Merge
+```
+## omnimergekit
+Tools for model merging, expert pruning, differential competence-map extraction, and GGUF quantization — [omnimergekit](https://github.com/mann1x/omnimergekit)