Instructions to use s-sahoo/uno-qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s-sahoo/uno-qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="s-sahoo/uno-qwen3-8B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("s-sahoo/uno-qwen3-8B", trust_remote_code=True, device_map="auto")

PEFT
How to use s-sahoo/uno-qwen3-8B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use s-sahoo/uno-qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s-sahoo/uno-qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s-sahoo/uno-qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/s-sahoo/uno-qwen3-8B

SGLang

How to use s-sahoo/uno-qwen3-8B 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 "s-sahoo/uno-qwen3-8B" \
    --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": "s-sahoo/uno-qwen3-8B",
		"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 "s-sahoo/uno-qwen3-8B" \
        --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": "s-sahoo/uno-qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use s-sahoo/uno-qwen3-8B with Docker Model Runner:
```
docker model run hf.co/s-sahoo/uno-qwen3-8B
```

uno-qwen3-8B / bundle_manifest.json

s-sahoo

Publish self-contained Uno Qwen3-8B inference bundle

3666d66 verified 13 days ago

Raw

History Blame

581 Bytes

	{
	"schema_version": 1,
	"layout": {
	"base": ".",
	"adapter": "adapter"
	},
	"base": {
	"repo_id": "IFM/uno-qwen3-8b-base",
	"revision": "4ccfeed3fba497e40495fe6dc5c15c89f7f1e2cd",
	"architecture": "SDARForCausalLM",
	"weight_dtype": "bfloat16",
	"weight_index": "model.safetensors.index.json",
	"weight_shards": 5
	},
	"adapter": {
	"repo_id": "s-sahoo/uno-qwen3-8B",
	"revision": "79536cf8c70aa48b9badc2532ffef208947463e3",
	"peft_type": "LORA",
	"rank": 128,
	"alpha": 2048,
	"weights": "adapter/adapter_model.safetensors"
	}
	}