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 Mar 17

Commit

2a03adb

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Update README.md

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@@ -33,7 +33,7 @@ A tool output pruner for coding agents. When an agent runs a tool (pytest, grep,
 Tool output (500 lines) → Squeez → Relevant lines (30 lines) → Agent context
 ```
-Existing context pruning tools ([SWE-Pruner](https://github.com/Ayanami1314/swe-pruner), [Zilliz Semantic Highlight](https://huggingface.co/zilliz/semantic-highlight-bilingual-v1), [Provence](https://arxiv.org/abs/2501.16214)) are built for source code or document paragraphs. They don't handle the mixed, unstructured format of tool output (stack traces interleaved with passing tests, grep matches with context lines, build logs with timestamps).
 This model is [Qwen 3.5 2B](https://huggingface.co/Qwen/Qwen3.5-2B) fine-tuned to extract verbatim relevant lines from tool output given a task-specific query. It's trained specifically on 14 types of tool output from real SWE-bench workflows.

 Tool output (500 lines) → Squeez → Relevant lines (30 lines) → Agent context
 ```
+Existing context pruning tools ([SWE-Pruner](https://github.com/Ayanami1314/swe-pruner), [Zilliz Semantic Highlight](https://huggingface.co/zilliz/semantic-highlight-bilingual-v1), [Provence](https://github.com/hotchpotch/open_provence)) are built for source code or document paragraphs. They don't handle the mixed, unstructured format of tool output (stack traces interleaved with passing tests, grep matches with context lines, build logs with timestamps).
 This model is [Qwen 3.5 2B](https://huggingface.co/Qwen/Qwen3.5-2B) fine-tuned to extract verbatim relevant lines from tool output given a task-specific query. It's trained specifically on 14 types of tool output from real SWE-bench workflows.