Instructions to use Jeesup/svdsafety_l2_remove40_whiten_protk8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jeesup/svdsafety_l2_remove40_whiten_protk8 with Transformers:

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

pipe = pipeline("text-generation", model="Jeesup/svdsafety_l2_remove40_whiten_protk8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jeesup/svdsafety_l2_remove40_whiten_protk8")
model = AutoModelForCausalLM.from_pretrained("Jeesup/svdsafety_l2_remove40_whiten_protk8", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jeesup/svdsafety_l2_remove40_whiten_protk8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jeesup/svdsafety_l2_remove40_whiten_protk8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jeesup/svdsafety_l2_remove40_whiten_protk8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jeesup/svdsafety_l2_remove40_whiten_protk8

SGLang

How to use Jeesup/svdsafety_l2_remove40_whiten_protk8 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 "Jeesup/svdsafety_l2_remove40_whiten_protk8" \
    --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": "Jeesup/svdsafety_l2_remove40_whiten_protk8",
		"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 "Jeesup/svdsafety_l2_remove40_whiten_protk8" \
        --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": "Jeesup/svdsafety_l2_remove40_whiten_protk8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jeesup/svdsafety_l2_remove40_whiten_protk8 with Docker Model Runner:
```
docker model run hf.co/Jeesup/svdsafety_l2_remove40_whiten_protk8
```

svdsafety_l2_remove40_whiten_protk8

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---
license: llama2
base_model: meta-llama/Llama-2-7b-chat-hf
library_name: transformers
pipeline_tag: text-generation
tags:
- llama2
- svd
- compression
- safety
- interpretability
---

# svdsafety_l2_remove40_whiten_protk8

A Llama-2-7b-chat checkpoint compressed with SVD-LLM to **0.0% of dense
parameters**, then given a **0.0% parameter budget** of restored SVD
components selected by the **`unknown`** rule.

This is a research artifact from a study of how SVD compression damages safety
behaviour and which component-selection rule best repairs it. It is one cell of a
grid over selection rules and budgets; it is **not** a general-purpose chat model.

## Provenance

| field | value |
|---|---|
| base (uncompressed) | `meta-llama/Llama-2-7b-chat-hf` |
| compression | SVD-LLM, 0.00% of parameters removed |
| selection rule | `unknown` |
| restore budget | 0.000% of dense parameters |
| components restored | 0 |
| components swapped out | 0 |
| resulting parameter fraction | 0.0000 |
| seed | 42 |



## Intended use and limitations

This checkpoint exists to measure safety/utility trade-offs under compression.
Several arms in the grid are **deliberately safety-degraded** relative to
Llama-2-7b-chat: compression alone raises attack-success rate, and the point of
the study is to quantify that and test recovery. Treat any given cell as an
experimental subject, not as a deployable assistant, and evaluate it yourself
before drawing conclusions from it.

## Licence

Llama 2 Community License. `LICENSE.txt` and `USE_POLICY.md` are included in this
repository, and use of this derivative is bound by both. Built with Llama 2.