Instructions to use Montalte/qwen4b-code-think-taskvector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Montalte/qwen4b-code-think-taskvector with Transformers:

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

pipe = pipeline("text-generation", model="Montalte/qwen4b-code-think-taskvector")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Montalte/qwen4b-code-think-taskvector")
model = AutoModelForCausalLM.from_pretrained("Montalte/qwen4b-code-think-taskvector", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Montalte/qwen4b-code-think-taskvector with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Montalte/qwen4b-code-think-taskvector"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Montalte/qwen4b-code-think-taskvector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Montalte/qwen4b-code-think-taskvector

SGLang

How to use Montalte/qwen4b-code-think-taskvector 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 "Montalte/qwen4b-code-think-taskvector" \
    --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": "Montalte/qwen4b-code-think-taskvector",
		"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 "Montalte/qwen4b-code-think-taskvector" \
        --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": "Montalte/qwen4b-code-think-taskvector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Montalte/qwen4b-code-think-taskvector with Docker Model Runner:
```
docker model run hf.co/Montalte/qwen4b-code-think-taskvector
```

qwen4b-code-think-taskvector / MANIFEST.sha256

Montalte

Upload qwen4b taskvector merge

f82d492 verified 27 days ago

Raw History Blame Contribute Delete

1.82 kB

	832dd9e00a68dd83b3c3fb9f5588dad7dcf337a0db50f7d9483f310cd292e92e LICENSE
	5e66b33663c05d41fe3758efd5dde9aaa83e587e032e6f4d458a6fc74bc881f9 OFFICIAL_MERGE_RECEIPT.json
	1592a771cd3fe8b9e6a976c36a2f8edbcd8c373b4ff5645c69911f0944802f33 README.md
	490367bf1e2bd55f5a7381dc4083bcffc18430bcbc26457680c5c17268225bcc adapter/MANIFEST.json
	16a5f02dafeca4c7804f7306e353375ac567d8158ed37baa20b80dcfe9188f33 adapter/TOKEN_ROWS_META.json
	4e04ab4bb3a660f837b6f57d3964cebfdf5b5a58cebc0c5cd9a30372a396212c adapter/adapter_config.json
	619dcee8c5c04416fed0cee928375583332ce2986bf3f51c0caf8408433831ed adapter/adapter_model.safetensors
	407274bf3280d79ed8efabaf3c3b3e75debaeb6b8efdb4433ac0bae934144688 adapter/token_rows_both_sides.safetensors
	87a2728cb8dc9fe424d624542f6060ec05a1d285ebbec578bb078900e33396b5 chat_template.jinja
	40bcf4e0a6a7b5eb8aa582ee23f0c9700d76f147d17159419893bbad0f762176 config.json
	a2ac0d07aac37502207a212b122abe32bd9564266594a317c0ff490ebce1f226 generation_config.json
	0e43b8415e406761c7ab73c8225fde51abdb7f51a4b80a9e90c616f53c3ea419 model.safetensors
	5ebf618c3d547252a8078665415fa4fb3b080d071c7e0f2750f6af44e231bc3e provenance/DEV256_COMPLETE.json
	24fc75c1ce08f0e4b7dcc31684c36c1f95b7032999c0e40308426781728f939c provenance/MQ0_NEAR_DUP.json
	c9f6e3b33a4693354da05fd6f18b3d05932430ca7c8dc9f7f008d3dcc49ac592 provenance/POLICY.json
	5c45fca93b73d0293c39913fc74160cc24f3ed5efa4bfd51582428cc029e1cd2 provenance/RUN_IDENTITY.json
	0304abe092d416ca7974b8f98f39783c52e71467bea8382ed000315ed917ec8a provenance/TRAINING_CONFIG.json
	2ff0f61e200d18dd55b1301f9573e923abd6fe87608220f7126cf709d4cb7288 provenance/build_mix_distill_payload.py
	be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506 tokenizer.json
	1749ac66e3e7ce3862337c23cca5895a2ee131929563e18a944f8c1c80363712 tokenizer_config.json