Instructions to use ryzax/qwen3_1.7B_sft_v4_1e-5_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryzax/qwen3_1.7B_sft_v4_1e-5_4 with Transformers:

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

pipe = pipeline("text-generation", model="ryzax/qwen3_1.7B_sft_v4_1e-5_4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ryzax/qwen3_1.7B_sft_v4_1e-5_4")
model = AutoModelForMultimodalLM.from_pretrained("ryzax/qwen3_1.7B_sft_v4_1e-5_4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ryzax/qwen3_1.7B_sft_v4_1e-5_4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryzax/qwen3_1.7B_sft_v4_1e-5_4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryzax/qwen3_1.7B_sft_v4_1e-5_4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryzax/qwen3_1.7B_sft_v4_1e-5_4

SGLang

How to use ryzax/qwen3_1.7B_sft_v4_1e-5_4 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 "ryzax/qwen3_1.7B_sft_v4_1e-5_4" \
    --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": "ryzax/qwen3_1.7B_sft_v4_1e-5_4",
		"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 "ryzax/qwen3_1.7B_sft_v4_1e-5_4" \
        --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": "ryzax/qwen3_1.7B_sft_v4_1e-5_4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryzax/qwen3_1.7B_sft_v4_1e-5_4 with Docker Model Runner:
```
docker model run hf.co/ryzax/qwen3_1.7B_sft_v4_1e-5_4
```

qwen3_1.7B_sft_v4_1e-5_4 / model-00002-of-00002.safetensors

zzzeeee

Model save

f5960cd verified about 1 year ago

Download

History Blame Contribute Delete

3.16 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:: 728421dd26ad45a86d00a3099493b6141e0302e0db55133b11b6dd6d92c5ae7a
Size of remote file:: 3.16 GB
SHA256:: 26a3d3fc1a80e00f29f94e383a2228692f0cb7b4f84f74d7c2a5c79b631be4ec

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