Instructions to use beyoru/Qwen3-CoderSmall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/Qwen3-CoderSmall with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/Qwen3-CoderSmall")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/Qwen3-CoderSmall")
model = AutoModelForCausalLM.from_pretrained("beyoru/Qwen3-CoderSmall", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beyoru/Qwen3-CoderSmall with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/beyoru/Qwen3-CoderSmall

SGLang

How to use beyoru/Qwen3-CoderSmall 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 "beyoru/Qwen3-CoderSmall" \
    --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": "beyoru/Qwen3-CoderSmall",
		"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 "beyoru/Qwen3-CoderSmall" \
        --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": "beyoru/Qwen3-CoderSmall",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/Qwen3-CoderSmall with Docker Model Runner:
```
docker model run hf.co/beyoru/Qwen3-CoderSmall
```

beyoru commited on Aug 22, 2025

Commit

aa48de2

verified ·

1 Parent(s): 39bf5ff

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -39

README.md CHANGED Viewed

@@ -8,42 +8,3 @@ tags:
 - merge
 ---
-# merged_model
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
-### Models Merged
-The following models were included in the merge:
-* [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
-* [suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-slices:
-  - sources:
-      - model: Qwen/Qwen3-0.6B
-        layer_range: [0, 28]
-      - model: suayptalha/Qwen3-0.6B-Code-Expert
-        layer_range: [0, 28]
-merge_method: slerp
-base_model: Qwen/Qwen3-0.6B
-parameters:
-  t:
-    - filter: self_attn
-      value: [0, 0.5, 0.3, 0.7, 1]
-    - filter: mlp
-      value: [1, 0.5, 0.7, 0.3, 0]
-    - value: 0.5
-dtype: bfloat16
-```


8	- merge
9
10	---