Instructions to use leapeto/Qwen3-4B-AbstractCoT-warmup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leapeto/Qwen3-4B-AbstractCoT-warmup with Transformers:

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

pipe = pipeline("text-generation", model="leapeto/Qwen3-4B-AbstractCoT-warmup")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("leapeto/Qwen3-4B-AbstractCoT-warmup", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use leapeto/Qwen3-4B-AbstractCoT-warmup with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/leapeto/Qwen3-4B-AbstractCoT-warmup

SGLang

How to use leapeto/Qwen3-4B-AbstractCoT-warmup 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 "leapeto/Qwen3-4B-AbstractCoT-warmup" \
    --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": "leapeto/Qwen3-4B-AbstractCoT-warmup",
		"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 "leapeto/Qwen3-4B-AbstractCoT-warmup" \
        --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": "leapeto/Qwen3-4B-AbstractCoT-warmup",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use leapeto/Qwen3-4B-AbstractCoT-warmup with Docker Model Runner:
```
docker model run hf.co/leapeto/Qwen3-4B-AbstractCoT-warmup
```

Qwen3-4B-AbstractCoT-warmup / train_logs /pi1_phaseA.json

leapeto

Add files using upload-large-folder tool

a555798 verified 12 days ago

raw

history blame contribute delete

1.77 kB

	{
	"losses": [
	3.488373909890652,
	2.407912985235453,
	1.7763818703591823,
	1.4919575069099664,
	0.9603039052337408,
	0.9057566102594137,
	0.8390422958880663,
	0.9064983982592821,
	0.7787921145558357,
	0.78125288952142,
	0.8250404935330152,
	0.8201050635427236,
	0.8527244713157416,
	0.7438110310584307,
	0.9024393826723098,
	0.8181035034358501,
	0.8188389737159014,
	0.8235138654708862,
	0.8209620092064143,
	0.8652047950774431,
	0.7606889367103576,
	0.884891077503562,
	0.7458537224680185,
	0.9200425513088704,
	0.8810815073549747,
	0.8085172940045595,
	0.8518438508734107,
	0.813002785295248,
	0.8703699089586735,
	0.8568720713257789,
	0.8453036542981863
	],
	"lrs": [
	6.666666666666667e-05,
	9.993008576227247e-05,
	9.937194443381972e-05,
	9.826190093588563e-05,
	9.661236384224129e-05,
	9.444177243274618e-05,
	9.177439057064683e-05,
	8.864003547001915e-05,
	8.507374438531607e-05,
	8.111538294891684e-05,
	7.680919953486048e-05,
	7.220333063028872e-05,
	6.734926274378312e-05,
	6.230125686563068e-05,
	5.7115741913664264e-05,
	5.185068394501791e-05,
	4.6564938185035956e-05,
	4.131759111665349e-05,
	3.616729998467365e-05,
	3.1171637098265064e-05,
	2.638644626136587e-05,
	2.1865218525109495e-05,
	1.7658494240397126e-05,
	1.3813298094746491e-05,
	1.037261344883343e-05,
	7.374901848832683e-06,
	4.853673085668947e-06,
	2.8371106072518195e-06,
	1.3477564710088098e-06,
	4.02259358460233e-07,
	1.1188468644907079e-08
	],
	"wallclock_s": 10715,
	"n_examples": 5000,
	"epochs": 1,
	"mode": "bottleneck",
	"lora_rank": 32,
	"total_opt_steps": 156,
	"num_processes": 2
	}