Instructions to use methil-group/nexus-flash-9B-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use methil-group/nexus-flash-9B-mlx-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("methil-group/nexus-flash-9B-mlx-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use methil-group/nexus-flash-9B-mlx-4bit with Transformers:

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

pipe = pipeline("text-generation", model="methil-group/nexus-flash-9B-mlx-4bit")
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-mlx-4bit")
model = AutoModelForMultimodalLM.from_pretrained("methil-group/nexus-flash-9B-mlx-4bit")
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
LM Studio

vLLM

How to use methil-group/nexus-flash-9B-mlx-4bit 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-mlx-4bit"
# 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-mlx-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/methil-group/nexus-flash-9B-mlx-4bit

SGLang

How to use methil-group/nexus-flash-9B-mlx-4bit 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-mlx-4bit" \
    --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-mlx-4bit",
		"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 "methil-group/nexus-flash-9B-mlx-4bit" \
        --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-mlx-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use methil-group/nexus-flash-9B-mlx-4bit 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-mlx-4bit 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-mlx-4bit 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-mlx-4bit 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-mlx-4bit",
    max_seq_length=2048,
)

How to use methil-group/nexus-flash-9B-mlx-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "methil-group/nexus-flash-9B-mlx-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "methil-group/nexus-flash-9B-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use methil-group/nexus-flash-9B-mlx-4bit 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 "methil-group/nexus-flash-9B-mlx-4bit"

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 methil-group/nexus-flash-9B-mlx-4bit

Run Hermes

hermes

OpenClaw new

How to use methil-group/nexus-flash-9B-mlx-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "methil-group/nexus-flash-9B-mlx-4bit"

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 "methil-group/nexus-flash-9B-mlx-4bit" \
  --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"

MLX LM

How to use methil-group/nexus-flash-9B-mlx-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "methil-group/nexus-flash-9B-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "methil-group/nexus-flash-9B-mlx-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "methil-group/nexus-flash-9B-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use methil-group/nexus-flash-9B-mlx-4bit with Docker Model Runner:
```
docker model run hf.co/methil-group/nexus-flash-9B-mlx-4bit
```

ethanzxv commited on Apr 22

Commit

05ab6f4

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1 Parent(s): fa1d57a

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@@ -14,3 +14,46 @@ language:
 library_name: mlx
 pipeline_tag: text-generation
 ---

 library_name: mlx
 pipeline_tag: text-generation
 ---
+![image](https://cdn-uploads.huggingface.co/production/uploads/670641521fbaec252ff5012a/A-xPsLZCg0t7_rXDMYeQ8.png)
+# Nexus-Flash-9B-MLX-4bit
+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.