Instructions to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit 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("Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit")

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 Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with Transformers:

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

pipe = pipeline("text-generation", model="Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit")
model = AutoModelForImageTextToText.from_pretrained("Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit")
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
LM Studio

vLLM

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit

SGLang

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit 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 "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit" \
    --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": "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit",
		"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 "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit" \
        --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": "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit 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 Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit 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 Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit",
    max_seq_length=2048,
)

Pi new

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"

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": "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit 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 "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"

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 Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit

Run Hermes

hermes

MLX LM

How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit with Docker Model Runner:
```
docker model run hf.co/Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit
```

MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit / README.md

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metadata

base_model: Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen3_5
  - reasoning
  - distillation
  - deepseek
  - deepseek-v4
  - sft
  - long-cot
  - chain-of-thought
  - efficient-inference
  - agent
  - multilingual
  - mlx
license: apache-2.0
language:
  - en
  - zh
  - ko
  - ja
  - es
  - ru
pipeline_tag: text-generation
datasets:
  - Jackrong/DeepSeek-V4-Distill-8000x
library_name: mlx

Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit

This model Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit was converted to MLX format from Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash using mlx-lm version 0.30.7.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Jackrong/MLX-Qwen3.5-9B-DeepSeek-V4-Flash-6bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

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