Instructions to use KRLabsOrg/squeez-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KRLabsOrg/squeez-2b with Transformers:

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

pipe = pipeline("text-generation", model="KRLabsOrg/squeez-2b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("KRLabsOrg/squeez-2b")
model = AutoModelForImageTextToText.from_pretrained("KRLabsOrg/squeez-2b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KRLabsOrg/squeez-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KRLabsOrg/squeez-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KRLabsOrg/squeez-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KRLabsOrg/squeez-2b

SGLang

How to use KRLabsOrg/squeez-2b 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 "KRLabsOrg/squeez-2b" \
    --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": "KRLabsOrg/squeez-2b",
		"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 "KRLabsOrg/squeez-2b" \
        --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": "KRLabsOrg/squeez-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KRLabsOrg/squeez-2b with Docker Model Runner:
```
docker model run hf.co/KRLabsOrg/squeez-2b
```

adaamko commited on Apr 8

Commit

3a4c2e6

verified ·

1 Parent(s): 85f4123

Add arXiv paper link and citation (2604.04979)

Browse files

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -32,7 +32,7 @@ Tool output (500 lines) → Squeez → Relevant lines (30 lines) → Agent conte
 - Works as CLI pipe, Python library, or vLLM server
 - Trained on **27 tool types** from real SWE-bench workflows and synthetic multi-ecosystem outputs
-**Resources:** [Paper (coming soon)]() | [Dataset](https://huggingface.co/datasets/KRLabsOrg/tool-output-extraction-swebench) | [Code & CLI](https://github.com/KRLabsOrg/squeez) | [Blog post](https://huggingface.co/blog/KRLabsOrg/squeez)
 ## Results
@@ -196,10 +196,13 @@ Apache 2.0
 ## Citation
 ```bibtex
-@misc{kovacs2026squeez,
-    title={Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents},
-    author={Adam Kovacs},
-    year={2026},
-    url={https://github.com/KRLabsOrg/squeez}
 }
 ```

 - Works as CLI pipe, Python library, or vLLM server
 - Trained on **27 tool types** from real SWE-bench workflows and synthetic multi-ecosystem outputs
+**Resources:** [Paper](https://arxiv.org/abs/2604.04979) | [Dataset](https://huggingface.co/datasets/KRLabsOrg/tool-output-extraction-swebench) | [Code & CLI](https://github.com/KRLabsOrg/squeez) | [Blog post](https://huggingface.co/blog/KRLabsOrg/squeez)
 ## Results
 ## Citation
 ```bibtex
+@misc{kovács2026squeeztaskconditionedtooloutputpruning,
+      title={Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents},
+      author={Ádám Kovács},
+      year={2026},
+      eprint={2604.04979},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2604.04979},
 }
 ```