Instructions to use Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42 with Transformers:

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

pipe = pipeline("text-generation", model="Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42")
model = AutoModelForCausalLM.from_pretrained("Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42", 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/llama2_7b_chat_svdllm_v2_remove_40_seed42 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42"
# 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/llama2_7b_chat_svdllm_v2_remove_40_seed42",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42

SGLang

How to use Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42 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/llama2_7b_chat_svdllm_v2_remove_40_seed42" \
    --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/llama2_7b_chat_svdllm_v2_remove_40_seed42",
		"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/llama2_7b_chat_svdllm_v2_remove_40_seed42" \
        --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/llama2_7b_chat_svdllm_v2_remove_40_seed42",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42 with Docker Model Runner:
```
docker model run hf.co/Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42
```

llama2_7b_chat_svdllm_v2_remove_40_seed42

A Llama-2-7b-chat checkpoint compressed with SVD-LLM V2 (arXiv 2503.12340), paper-derived reimplementation -- see the reproduction row below to 60.0% of dense parameters, then recovered with SVD-LLM's stage-2 LoRA (sequential U then V, alpaca-cleaned, r=8, alpha=16, 2 epochs, lr 1e-4, batch 64, cutoff 256, bf16).

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 V2 (arXiv 2503.12340), paper-derived reimplementation -- see the reproduction row below, 40.00% of parameters removed
selection rule	`unknown`
restore budget	0.000% of dense parameters
components restored	0
components swapped out	0
resulting parameter fraction	0.5998
seed	42
algorithm 1	heterogeneous per-matrix compression ratio from the theoretical truncation loss (grouped by projection type)
algorithm 2	eigendecomposition square root of X^T X in place of upstream's Cholesky factor
rank range	204-1978 across 224 matrices (uniform SVD-LLM would use one rank per shape)
matrices at the keep floor	3 (0.k_proj, 0.o_proj, 0.q_proj)
deviation from the paper	Algorithm 1 as printed is unbounded and can emit a reduction ratio > 1 (layer-0 q/k/o_proj do, at every ratio). Capped at keep >= 0.10 with the excess redistributed across the group in proportion to score, so each group's mean reduction stays exactly R. The paper specifies no bound.
upstream V2 source	AIoT-MLSys-Lab/SVD-LLM advertises the V2 paper but ships no V2 source (checked at commit 7538cca, 2025-08-27); Algorithms 1 and 2 here are implemented from the paper text
reproduction gate	LLaMA-7B @ 20% removed, WikiText-2 PPL: this implementation 7.913 vs the paper's reported 7.12 for V2 and 7.94 for v1. A uniform-rank control through the identical pipeline gives 7.901, so these cells reproduce v1's number, not V2's, and Algorithm 1 contributes -0.012 there. On Llama-2-7b-chat the allocation does help slightly (PPL -0.06 to -0.26 vs v1 at 20-40% removal). Treat this as V2-as-described, not V2-as-published-numbers.

Measured

metric	value
AdvBench ASR (HarmBench judge)	0.4019
StrongREJECT ASR (HarmBench judge)	0.2173
Macro over-refusal (WildGuard)	0.1274
WikiText-2 perplexity	11.0735

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 them. Built with Llama 2.

Downloads last month: 369

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Jeesup/llama2_7b_chat_svdllm_v2_remove_40_seed42

Base model

meta-llama/Llama-2-7b-chat-hf

Finetuned

(801)

this model