Instructions to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/root/.cache/huggingface/hub/models--Qwen--Qwen3.5-9B/snapshots/c202236235762e1c871ad0ccb60c8ee5ba337b9a")
model = PeftModel.from_pretrained(base_model, "AntimLabs/Qwen3.5-9B-LoRA-FlowMatching")

Transformers

How to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching with Transformers:

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

pipe = pipeline("text-generation", model="AntimLabs/Qwen3.5-9B-LoRA-FlowMatching")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AntimLabs/Qwen3.5-9B-LoRA-FlowMatching", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AntimLabs/Qwen3.5-9B-LoRA-FlowMatching

SGLang

How to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching 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 "AntimLabs/Qwen3.5-9B-LoRA-FlowMatching" \
    --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": "AntimLabs/Qwen3.5-9B-LoRA-FlowMatching",
		"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 "AntimLabs/Qwen3.5-9B-LoRA-FlowMatching" \
        --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": "AntimLabs/Qwen3.5-9B-LoRA-FlowMatching",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AntimLabs/Qwen3.5-9B-LoRA-FlowMatching with Docker Model Runner:
```
docker model run hf.co/AntimLabs/Qwen3.5-9B-LoRA-FlowMatching
```

Qwen3.5-9B-LoRA-FlowMatching / action_decoder.pt

g8967

Upload final flow matching checkpoint from blob

9508d10 verified about 1 month ago

download

history blame contribute delete

1.28 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 0b7e59063f5e3e6eef2165d401428a5c59d58cfa01eeb5fb34c78d7ea705b016
Size of remote file:: 1.28 GB
SHA256:: d1f830fcc88e0b755b0ec29ed1071b02cd5c7c1f4dd6912cfcf4703451671242

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.