Instructions to use stellia/stellialm_mini_qwen_9tasks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stellia/stellialm_mini_qwen_9tasks with Transformers:

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

pipe = pipeline("text-generation", model="stellia/stellialm_mini_qwen_9tasks")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stellia/stellialm_mini_qwen_9tasks")
model = AutoModelForCausalLM.from_pretrained("stellia/stellialm_mini_qwen_9tasks", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stellia/stellialm_mini_qwen_9tasks with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stellia/stellialm_mini_qwen_9tasks"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stellia/stellialm_mini_qwen_9tasks",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stellia/stellialm_mini_qwen_9tasks

SGLang

How to use stellia/stellialm_mini_qwen_9tasks 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 "stellia/stellialm_mini_qwen_9tasks" \
    --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": "stellia/stellialm_mini_qwen_9tasks",
		"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 "stellia/stellialm_mini_qwen_9tasks" \
        --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": "stellia/stellialm_mini_qwen_9tasks",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stellia/stellialm_mini_qwen_9tasks with Docker Model Runner:
```
docker model run hf.co/stellia/stellialm_mini_qwen_9tasks
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Public version of our 3b-model trained to perform 9 specific task in english and french with high accuracy. The benchmark bellow is done with our evaluation pipeline.

Model Fine-tune from Qwen/Qwen2.5-3B-Instruct with a specific LoRA adapter.

The prompt of our 9 specific task might follow in a next post. The goal of our team is to specialized small LLM on our clients specific needs while keeping the highest quality on general tasks.

Our 3b models compete with GPT-mini on those specific tasks.

+----------------------+---------+------------+------------+
|        qwen3b        | Overall | Team score | loads fail |
+----------------------+---------+------------+------------+
| answer_reformulation |   0.73  |    0.74    |    0.99    |
| query_reformulation  |   0.83  |    0.99    |     0      |
|    summarization     |   0.92  |    ---     |     0      |
|  keyword_extraction  |   0.78  |    ---     |     0      |
|  fill_in_generation  |   0.86  |    ---     |     0      |
|    keyword_update    |   0.66  |    0.89    |     2      |
|         gqa          |   0.81  |    0.63    |     3      |
|      true_false      |   0.77  |    ---     |     0      |
|         mcq          |   0.86  |    ---     |     0      |
|        Total         |   0.8   |    ---     |    5.99    |
+----------------------+---------+------------+------------+

Downloads last month: 14

Safetensors

Model size

3B params

Tensor type

F32

Model tree for stellia/stellialm_mini_qwen_9tasks

Quantizations

3 models