Instructions to use modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base with Transformers:

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

pipe = pipeline("text-generation", model="modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base"
# 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/lingcoder_shortcot_4k_lora_qwen3_4b_base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base

SGLang

How to use modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base 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/lingcoder_shortcot_4k_lora_qwen3_4b_base" \
    --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/lingcoder_shortcot_4k_lora_qwen3_4b_base",
		"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/lingcoder_shortcot_4k_lora_qwen3_4b_base" \
        --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/lingcoder_shortcot_4k_lora_qwen3_4b_base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base with Docker Model Runner:
```
docker model run hf.co/modrill/lingcoder_shortcot_4k_lora_qwen3_4b_base
```

modrill commited on 12 days ago

Commit

fa42872

verified ·

1 Parent(s): 944a1b0

Add model card

Browse files

Files changed (1) hide show

README.md +10 -56

README.md CHANGED Viewed

@@ -1,62 +1,16 @@
 ---
-base_model: Qwen/Qwen3-4B-Base
-library_name: peft
-model_name: qwen3-4b-base-restart-lingcoder-shortcot-4k-lora
 tags:
-- base_model:adapter:Qwen/Qwen3-4B-Base
-- lora
-- sft
-- transformers
-- trl
-licence: license
-pipeline_tag: text-generation
 ---
-# Model Card for qwen3-4b-base-restart-lingcoder-shortcot-4k-lora
-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
-- PEFT 0.19.1
-- 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
 ---
+# lingcoder_shortcot_4k_lora_qwen3_4b_base
+Auto-uploaded from local training output.
+- Source path: `trl/models/qwen3-4b-base-restart-lingcoder-shortcot-4k-lora`
+- Uploaded at: `2026-05-20T05:46:40.204294`
+- Visibility: `public`