- Libraries
- MLX
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
