Instructions to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Base")
model = PeftModel.from_pretrained(base_model, "modrill/Qwen3-4B-Base-ThinkCode-A-U025")

Transformers

How to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 with Transformers:

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

pipe = pipeline("text-generation", model="modrill/Qwen3-4B-Base-ThinkCode-A-U025")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("modrill/Qwen3-4B-Base-ThinkCode-A-U025", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/Qwen3-4B-Base-ThinkCode-A-U025"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modrill/Qwen3-4B-Base-ThinkCode-A-U025",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/modrill/Qwen3-4B-Base-ThinkCode-A-U025

SGLang

How to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 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 "modrill/Qwen3-4B-Base-ThinkCode-A-U025" \
    --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": "modrill/Qwen3-4B-Base-ThinkCode-A-U025",
		"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 "modrill/Qwen3-4B-Base-ThinkCode-A-U025" \
        --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": "modrill/Qwen3-4B-Base-ThinkCode-A-U025",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use modrill/Qwen3-4B-Base-ThinkCode-A-U025 with Docker Model Runner:
```
docker model run hf.co/modrill/Qwen3-4B-Base-ThinkCode-A-U025
```

Qwen3-4B-Base-ThinkCode-A-U025 / ADAPTER_MANIFEST.json

modrill

Add files using upload-large-folder tool

4cd3552 verified 6 days ago

Raw

History Blame Contribute Delete

1.87 kB

{"adapter_model":{"bytes":607652912,"path":"adapter_model.safetensors","sha256":"e23f1eb30f6b2f3677dde70f250aea6fe11268f67f135e42425e3355c7f56822"},"arm_id":"A-U025","base_model":"Qwen/Qwen3-4B-Base","base_revision":"906bfd4b4dc7f14ee4320094d8b41684abff8539","contains_base_layer_tensor":false,"contains_base_model_shards":false,"contains_tokenizer_files":false,"created_at_utc":"2026-08-22T18:05:58Z","format":"PEFT_LORA_ADAPTER_ONLY","payload_files":[{"bytes":1519,"path":".gitattributes","sha256":"11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361"},{"bytes":11343,"path":"LICENSE","sha256":"832dd9e00a68dd83b3c3fb9f5588dad7dcf337a0db50f7d9483f310cd292e92e"},{"bytes":82204,"path":"MODULE_SCALE_MANIFEST.json","sha256":"8c317803003c550eb69d04bd0cb31c191d8d05dd8c8e8fcaedf19d8bf1573391"},{"bytes":4670,"path":"README.md","sha256":"56b9ef75e78ad1bd356d5a11e463ae419523d55739737a5618d4a2536e20e922"},{"bytes":1815,"path":"VALIDATION.json","sha256":"077cc8ee8883353791db34445b2489c5629ac2779d447e91f60891540614d97d"},{"bytes":1209,"path":"adapter_config.json","sha256":"7845c0b42c88faa7cdd3dec89e223b782ab98ac7786e5f789e60ea596bff8555"},{"bytes":607652912,"path":"adapter_model.safetensors","sha256":"e23f1eb30f6b2f3677dde70f250aea6fe11268f67f135e42425e3355c7f56822"}],"payload_manifest_sha256":"f3bf93e8df98b4b70402f7c3bc3ab7d633074ed9bdf5985dfba6dff515a8c426","repo_id":"modrill/Qwen3-4B-Base-ThinkCode-A-U025","scale_contract":{"body":0.25,"lm_head_and_tied_embedding":0.25},"schema":"HF_PEFT_ADAPTER_RELEASE_MANIFEST_V1","source_artifact_receipt_sha256":"2d85f3af2c7461a42889eb6bf5c61161391d464fae7aaa90f7d8aab40a90f6c8","source_original_adapter_sha256":"2eea0b87617f8b1abdfdb476b1521c70df30402fec638777a694b9d03858ee36","source_phase_a_adapter_recursive_sha256":"2a7be2a09738df9e78ec4b9c99bfcb18dff2c580c680976c6dd913bba764f0eb","status":"READY"}