Instructions to use andresnowak/Qwen3-0.6B-instruction-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andresnowak/Qwen3-0.6B-instruction-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="andresnowak/Qwen3-0.6B-instruction-finetuned")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andresnowak/Qwen3-0.6B-instruction-finetuned")
model = AutoModelForCausalLM.from_pretrained("andresnowak/Qwen3-0.6B-instruction-finetuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andresnowak/Qwen3-0.6B-instruction-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andresnowak/Qwen3-0.6B-instruction-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andresnowak/Qwen3-0.6B-instruction-finetuned

SGLang

How to use andresnowak/Qwen3-0.6B-instruction-finetuned 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 "andresnowak/Qwen3-0.6B-instruction-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "andresnowak/Qwen3-0.6B-instruction-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use andresnowak/Qwen3-0.6B-instruction-finetuned with Docker Model Runner:
```
docker model run hf.co/andresnowak/Qwen3-0.6B-instruction-finetuned
```

andresnowak commited on Jun 2, 2025

Commit

dadea84

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Update README.md

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@@ -19,6 +19,8 @@ language:
 This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 ```python

 This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
+This model was done using Language modelling (loss done on prompt and completion) Supervised instruction finetuning.
 ## Quick start
 ```python