Instructions to use pymlex/qwen3-4b-future-engineers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pymlex/qwen3-4b-future-engineers with Transformers:

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

pipe = pipeline("text-generation", model="pymlex/qwen3-4b-future-engineers")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pymlex/qwen3-4b-future-engineers", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pymlex/qwen3-4b-future-engineers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pymlex/qwen3-4b-future-engineers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pymlex/qwen3-4b-future-engineers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pymlex/qwen3-4b-future-engineers

SGLang

How to use pymlex/qwen3-4b-future-engineers 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 "pymlex/qwen3-4b-future-engineers" \
    --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": "pymlex/qwen3-4b-future-engineers",
		"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 "pymlex/qwen3-4b-future-engineers" \
        --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": "pymlex/qwen3-4b-future-engineers",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use pymlex/qwen3-4b-future-engineers with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pymlex/qwen3-4b-future-engineers to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pymlex/qwen3-4b-future-engineers to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pymlex/qwen3-4b-future-engineers to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="pymlex/qwen3-4b-future-engineers",
    max_seq_length=2048,
)

Docker Model Runner
How to use pymlex/qwen3-4b-future-engineers with Docker Model Runner:
```
docker model run hf.co/pymlex/qwen3-4b-future-engineers
```

pymlex commited on Mar 29

Commit

486483c

verified ·

1 Parent(s): ca8b7a4

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -24,7 +24,7 @@ This qwen3 model was trained with [Unsloth](https://github.com/unslothai/unsloth
 # Inference
-Get the model
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -47,7 +47,7 @@ model = PeftModel.from_pretrained(model, adapter_path)
 model.eval()
 ```
-Use this function to generate descriptions
 ```python
 system_prompt = """
@@ -86,7 +86,7 @@ def generate(topic):
     print(decoded.strip())
 ```
-Example
 ```python
 generate("Система мониторинга качества воздуха в школьных кабинетах")

 # Inference
+Get the model from HF.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model.eval()
 ```
+Use this function to generate descriptions.
 ```python
 system_prompt = """
     print(decoded.strip())
 ```
+Example.
 ```python
 generate("Система мониторинга качества воздуха в школьных кабинетах")