Instructions to use methil-group/nexus-flash-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use methil-group/nexus-flash-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="methil-group/nexus-flash-9B")
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("methil-group/nexus-flash-9B")
model = AutoModelForMultimodalLM.from_pretrained("methil-group/nexus-flash-9B")
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 methil-group/nexus-flash-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "methil-group/nexus-flash-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "methil-group/nexus-flash-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/methil-group/nexus-flash-9B

SGLang

How to use methil-group/nexus-flash-9B 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 "methil-group/nexus-flash-9B" \
    --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": "methil-group/nexus-flash-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "methil-group/nexus-flash-9B" \
        --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": "methil-group/nexus-flash-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use methil-group/nexus-flash-9B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for methil-group/nexus-flash-9B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for methil-group/nexus-flash-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for methil-group/nexus-flash-9B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="methil-group/nexus-flash-9B",
    max_seq_length=2048,
)

Docker Model Runner
How to use methil-group/nexus-flash-9B with Docker Model Runner:
```
docker model run hf.co/methil-group/nexus-flash-9B
```

nexus-flash-9B / README.md

ethanzxv

Update README.md

74a7eb6 verified 3 months ago

preview code

Raw

History Blame Contribute Delete

2.74 kB

	---
	base_model: unsloth/Qwen3.5-9B
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen3_5
	- agent
	- reasoning
	license: apache-2.0
	language:
	- en
	---

	![image](https://cdn-uploads.huggingface.co/production/uploads/670641521fbaec252ff5012a/A-xPsLZCg0t7_rXDMYeQ8.png)

	# Nexus-Flash-9B

	This model is a fine-tuned version of unsloth/Qwen3.5-9B, optimized for agent-based reasoning tasks. It was trained using the [Unsloth](https://github.com/unslothai/unsloth) framework to achieve faster training speeds and memory efficiency.

	## 📋 Model Details

	- Developed by: ethanzxv
	- Base Model: [unsloth/Qwen3.5-9B](https://huggingface.co/unsloth/Qwen3.5-9B)
	- License: Apache-2.0
	- Language: English
	- Finetuning Dataset: [lambda/hermes-agent-reasoning-traces](https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces)

	## 🚀 Training & Optimization

	This model was trained 2x faster using [Unsloth](https://github.com/unslothai/unsloth) combined with Hugging Face's TRL library. Unsloth allows for efficient fine-tuning of Large Language Models (LLMs) with significantly reduced VRAM usage and increased throughput.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

	### Dataset Information
	The model was fine-tuned on the Hermes Agent Reasoning Traces dataset. This dataset focuses on enhancing the model's ability to perform complex reasoning steps, particularly in agentic workflows, by providing detailed traces of thought processes and decision-making paths.

	- Dataset: [lambda/hermes-agent-reasoning-traces](https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces)

	## 🎯 Intended Use & Capabilities

	This model is designed for:
	- Agent Reasoning: Improved performance in tasks requiring multi-step logical deduction.
	- Complex Problem Solving: Better handling of intricate queries that require chain-of-thought processing.
	- General Text Generation: Maintains the strong general capabilities of the base Qwen3.5-9B model.

	## 📄 License

	This model is released under the Apache-2.0 license. Please refer to the base model's license and the dataset's license for any additional restrictions or requirements.

	## 🙏 Acknowledgements

	- [Unsloth](https://github.com/unslothai/unsloth): For providing the efficient fine-tuning framework.
	- [Hugging Face TRL](https://huggingface.co/docs/trl): For the training reinforcement library.
	- [Lambda](https://huggingface.co/lambda): For curating the Hermes Agent Reasoning Traces dataset.
	- [Alibaba Cloud](https://huggingface.co/Qwen): For the original Qwen3.5 base model.