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
```

ethanzxv commited on Apr 15

Commit

74a7eb6

verified ·

1 Parent(s): 3d6542a

Update README.md

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@@ -5,17 +5,52 @@ tags:
 - transformers
 - unsloth
 - qwen3_5
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded finetuned  model
 - **Developed by:** ethanzxv
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3.5-9B
-This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - 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.