Instructions to use modrill/kodcode_3_qwen3_4b_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/kodcode_3_qwen3_4b_sft with Transformers:

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

pipe = pipeline("text-generation", model="modrill/kodcode_3_qwen3_4b_sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/kodcode_3_qwen3_4b_sft")
model = AutoModelForCausalLM.from_pretrained("modrill/kodcode_3_qwen3_4b_sft")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use modrill/kodcode_3_qwen3_4b_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/kodcode_3_qwen3_4b_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modrill/kodcode_3_qwen3_4b_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/kodcode_3_qwen3_4b_sft

SGLang

How to use modrill/kodcode_3_qwen3_4b_sft 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/kodcode_3_qwen3_4b_sft" \
    --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": "modrill/kodcode_3_qwen3_4b_sft",
		"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 "modrill/kodcode_3_qwen3_4b_sft" \
        --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": "modrill/kodcode_3_qwen3_4b_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/kodcode_3_qwen3_4b_sft with Docker Model Runner:
```
docker model run hf.co/modrill/kodcode_3_qwen3_4b_sft
```

modrill commited on 10 days ago

Commit

5fab98f

verified ·

1 Parent(s): 8a56f98

Add model card

Browse files

Files changed (1) hide show

README.md +10 -52

README.md CHANGED Viewed

@@ -1,58 +1,16 @@
 ---
-base_model: Qwen/Qwen3-4B-Base
-library_name: transformers
-model_name: qwen3-4b-sft-kodcode-3
 tags:
-- generated_from_trainer
-- sft
-- trl
-licence: license
 ---
-# Model Card for qwen3-4b-sft-kodcode-3
-This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-## Quick start
-```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-## Training procedure
-This model was trained with SFT.
-### Framework versions
-- TRL: 1.5.0.dev0
-- Transformers: 5.8.0
-- Pytorch: 2.11.0
-- Datasets: 4.8.5
-- Tokenizers: 0.22.2
-## Citations
-Cite TRL as:
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
-```

 ---
+license: cc-by-nc-4.0
 tags:
+- qwen3
+- mhm
+- text-generation
+library_name: transformers
 ---
+# kodcode_3_qwen3_4b_sft
+Auto-uploaded from local training output.
+- Source path: `trl/qwen3-4b-sft-kodcode-3`
+- Uploaded at: `2026-05-20T05:55:31.724909`
+- Visibility: `public`