Instructions to use MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8 with Transformers:

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

pipe = pipeline("text-generation", model="MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8

SGLang

How to use MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8 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 "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8" \
    --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": "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8",
		"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 "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8" \
        --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": "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8 with Docker Model Runner:
```
docker model run hf.co/MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8
```

unsloth/llama-2-7b — uniform search — 0.8 target ratio

This model was compressed using kfac_svd with uniform rank search starting from unsloth/llama-2-7b as base model. You may check out our publication and project page for details on kfac-svd and our LEMS rank search.

Update (2026-10-23). The weights were recomputed with the fixed KFAC-SVD implementation. The first release of the code (v1, used for the published paper) whitened the output side of each weight with the Cholesky factor L of G = LLᵀ instead of Lᵀ. The correct version was described by the algorithm in the paper already. Those weights, which the paper's tables report, remain available as revision v1-paper: AutoModelForCausalLM.from_pretrained("MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8", revision="v1-paper", trust_remote_code=True).

Compression Details

Metric	Value
Base Model	`unsloth/llama-2-7b`
Method	`kfac_svd`
Search Method	`uniform`
Target Ratio	0.8
Compression Metric	`params`
Recommended Dtype	`float16`
Compressed Layers	224
Total Parameters	5,442,539,744

Usage

The checkpoint records its recommended dtype in config.json; no explicit torch_dtype argument should be needed with this remote-code wrapper. For standard Transformers models, torch_dtype="auto" is the portable fallback.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8",
    trust_remote_code=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8")

inputs = tokenizer('Hello, ', return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation Results

Dataset	Perplexity
wikitext2	7.31
ptb	53.86
c4	15.04

Zero-shot accuracy (LM-Eval, %):

Task	Accuracy
OpenBookQA	27.40
ARC-e	67.42
ARC-c	32.17
WinoGrande	64.33
HellaSwag	42.91
PIQA	70.40
Average	50.77

Rank Allocation

Per-layer ranks (click to expand)

Layer	Rank
`model.layers.0.mlp.down_proj`	2388
`model.layers.0.mlp.gate_proj`	2388
`model.layers.0.mlp.up_proj`	2388
`model.layers.0.self_attn.k_proj`	1638
`model.layers.0.self_attn.o_proj`	1638
`model.layers.0.self_attn.q_proj`	1638
`model.layers.0.self_attn.v_proj`	1638
`model.layers.1.mlp.down_proj`	2388
`model.layers.1.mlp.gate_proj`	2388
`model.layers.1.mlp.up_proj`	2388
`model.layers.1.self_attn.k_proj`	1638
`model.layers.1.self_attn.o_proj`	1638
`model.layers.1.self_attn.q_proj`	1638
`model.layers.1.self_attn.v_proj`	1638
`model.layers.10.mlp.down_proj`	2388
`model.layers.10.mlp.gate_proj`	2388
`model.layers.10.mlp.up_proj`	2388
`model.layers.10.self_attn.k_proj`	1638
`model.layers.10.self_attn.o_proj`	1638
`model.layers.10.self_attn.q_proj`	1638
`model.layers.10.self_attn.v_proj`	1638
`model.layers.11.mlp.down_proj`	2388
`model.layers.11.mlp.gate_proj`	2388
`model.layers.11.mlp.up_proj`	2388
`model.layers.11.self_attn.k_proj`	1638
`model.layers.11.self_attn.o_proj`	1638
`model.layers.11.self_attn.q_proj`	1638
`model.layers.11.self_attn.v_proj`	1638
`model.layers.12.mlp.down_proj`	2388
`model.layers.12.mlp.gate_proj`	2388
`model.layers.12.mlp.up_proj`	2388
`model.layers.12.self_attn.k_proj`	1638
`model.layers.12.self_attn.o_proj`	1638
`model.layers.12.self_attn.q_proj`	1638
`model.layers.12.self_attn.v_proj`	1638
`model.layers.13.mlp.down_proj`	2388
`model.layers.13.mlp.gate_proj`	2388
`model.layers.13.mlp.up_proj`	2388
`model.layers.13.self_attn.k_proj`	1638
`model.layers.13.self_attn.o_proj`	1638
`model.layers.13.self_attn.q_proj`	1638
`model.layers.13.self_attn.v_proj`	1638
`model.layers.14.mlp.down_proj`	2388
`model.layers.14.mlp.gate_proj`	2388
`model.layers.14.mlp.up_proj`	2388
`model.layers.14.self_attn.k_proj`	1638
`model.layers.14.self_attn.o_proj`	1638
`model.layers.14.self_attn.q_proj`	1638
`model.layers.14.self_attn.v_proj`	1638
`model.layers.15.mlp.down_proj`	2388
`model.layers.15.mlp.gate_proj`	2388
`model.layers.15.mlp.up_proj`	2388
`model.layers.15.self_attn.k_proj`	1638
`model.layers.15.self_attn.o_proj`	1638
`model.layers.15.self_attn.q_proj`	1638
`model.layers.15.self_attn.v_proj`	1638
`model.layers.16.mlp.down_proj`	2388
`model.layers.16.mlp.gate_proj`	2388
`model.layers.16.mlp.up_proj`	2388
`model.layers.16.self_attn.k_proj`	1638
`model.layers.16.self_attn.o_proj`	1638
`model.layers.16.self_attn.q_proj`	1638
`model.layers.16.self_attn.v_proj`	1638
`model.layers.17.mlp.down_proj`	2388
`model.layers.17.mlp.gate_proj`	2388
`model.layers.17.mlp.up_proj`	2388
`model.layers.17.self_attn.k_proj`	1638
`model.layers.17.self_attn.o_proj`	1638
`model.layers.17.self_attn.q_proj`	1638
`model.layers.17.self_attn.v_proj`	1638
`model.layers.18.mlp.down_proj`	2388
`model.layers.18.mlp.gate_proj`	2388
`model.layers.18.mlp.up_proj`	2388
`model.layers.18.self_attn.k_proj`	1638
`model.layers.18.self_attn.o_proj`	1638
`model.layers.18.self_attn.q_proj`	1638
`model.layers.18.self_attn.v_proj`	1638
`model.layers.19.mlp.down_proj`	2388
`model.layers.19.mlp.gate_proj`	2388
`model.layers.19.mlp.up_proj`	2388
`model.layers.19.self_attn.k_proj`	1638
`model.layers.19.self_attn.o_proj`	1638
`model.layers.19.self_attn.q_proj`	1638
`model.layers.19.self_attn.v_proj`	1638
`model.layers.2.mlp.down_proj`	2388
`model.layers.2.mlp.gate_proj`	2388
`model.layers.2.mlp.up_proj`	2388
`model.layers.2.self_attn.k_proj`	1638
`model.layers.2.self_attn.o_proj`	1638
`model.layers.2.self_attn.q_proj`	1638
`model.layers.2.self_attn.v_proj`	1638
`model.layers.20.mlp.down_proj`	2388
`model.layers.20.mlp.gate_proj`	2388
`model.layers.20.mlp.up_proj`	2388
`model.layers.20.self_attn.k_proj`	1638
`model.layers.20.self_attn.o_proj`	1638
`model.layers.20.self_attn.q_proj`	1638
`model.layers.20.self_attn.v_proj`	1638
`model.layers.21.mlp.down_proj`	2388
`model.layers.21.mlp.gate_proj`	2388
`model.layers.21.mlp.up_proj`	2388
`model.layers.21.self_attn.k_proj`	1638
`model.layers.21.self_attn.o_proj`	1638
`model.layers.21.self_attn.q_proj`	1638
`model.layers.21.self_attn.v_proj`	1638
`model.layers.22.mlp.down_proj`	2388
`model.layers.22.mlp.gate_proj`	2388
`model.layers.22.mlp.up_proj`	2388
`model.layers.22.self_attn.k_proj`	1638
`model.layers.22.self_attn.o_proj`	1638
`model.layers.22.self_attn.q_proj`	1638
`model.layers.22.self_attn.v_proj`	1638
`model.layers.23.mlp.down_proj`	2388
`model.layers.23.mlp.gate_proj`	2388
`model.layers.23.mlp.up_proj`	2388
`model.layers.23.self_attn.k_proj`	1638
`model.layers.23.self_attn.o_proj`	1638
`model.layers.23.self_attn.q_proj`	1638
`model.layers.23.self_attn.v_proj`	1638
`model.layers.24.mlp.down_proj`	2388
`model.layers.24.mlp.gate_proj`	2388
`model.layers.24.mlp.up_proj`	2388
`model.layers.24.self_attn.k_proj`	1638
`model.layers.24.self_attn.o_proj`	1638
`model.layers.24.self_attn.q_proj`	1638
`model.layers.24.self_attn.v_proj`	1638
`model.layers.25.mlp.down_proj`	2388
`model.layers.25.mlp.gate_proj`	2388
`model.layers.25.mlp.up_proj`	2388
`model.layers.25.self_attn.k_proj`	1638
`model.layers.25.self_attn.o_proj`	1638
`model.layers.25.self_attn.q_proj`	1638
`model.layers.25.self_attn.v_proj`	1638
`model.layers.26.mlp.down_proj`	2388
`model.layers.26.mlp.gate_proj`	2388
`model.layers.26.mlp.up_proj`	2388
`model.layers.26.self_attn.k_proj`	1638
`model.layers.26.self_attn.o_proj`	1638
`model.layers.26.self_attn.q_proj`	1638
`model.layers.26.self_attn.v_proj`	1638
`model.layers.27.mlp.down_proj`	2388
`model.layers.27.mlp.gate_proj`	2388
`model.layers.27.mlp.up_proj`	2388
`model.layers.27.self_attn.k_proj`	1638
`model.layers.27.self_attn.o_proj`	1638
`model.layers.27.self_attn.q_proj`	1638
`model.layers.27.self_attn.v_proj`	1638
`model.layers.28.mlp.down_proj`	2388
`model.layers.28.mlp.gate_proj`	2388
`model.layers.28.mlp.up_proj`	2388
`model.layers.28.self_attn.k_proj`	1638
`model.layers.28.self_attn.o_proj`	1638
`model.layers.28.self_attn.q_proj`	1638
`model.layers.28.self_attn.v_proj`	1638
`model.layers.29.mlp.down_proj`	2388
`model.layers.29.mlp.gate_proj`	2388
`model.layers.29.mlp.up_proj`	2388
`model.layers.29.self_attn.k_proj`	1638
`model.layers.29.self_attn.o_proj`	1638
`model.layers.29.self_attn.q_proj`	1638
`model.layers.29.self_attn.v_proj`	1638
`model.layers.3.mlp.down_proj`	2388
`model.layers.3.mlp.gate_proj`	2388
`model.layers.3.mlp.up_proj`	2388
`model.layers.3.self_attn.k_proj`	1638
`model.layers.3.self_attn.o_proj`	1638
`model.layers.3.self_attn.q_proj`	1638
`model.layers.3.self_attn.v_proj`	1638
`model.layers.30.mlp.down_proj`	2388
`model.layers.30.mlp.gate_proj`	2388
`model.layers.30.mlp.up_proj`	2388
`model.layers.30.self_attn.k_proj`	1638
`model.layers.30.self_attn.o_proj`	1638
`model.layers.30.self_attn.q_proj`	1638
`model.layers.30.self_attn.v_proj`	1638
`model.layers.31.mlp.down_proj`	2388
`model.layers.31.mlp.gate_proj`	2388
`model.layers.31.mlp.up_proj`	2388
`model.layers.31.self_attn.k_proj`	1638
`model.layers.31.self_attn.o_proj`	1638
`model.layers.31.self_attn.q_proj`	1638
`model.layers.31.self_attn.v_proj`	1638
`model.layers.4.mlp.down_proj`	2388
`model.layers.4.mlp.gate_proj`	2388
`model.layers.4.mlp.up_proj`	2388
`model.layers.4.self_attn.k_proj`	1638
`model.layers.4.self_attn.o_proj`	1638
`model.layers.4.self_attn.q_proj`	1638
`model.layers.4.self_attn.v_proj`	1638
`model.layers.5.mlp.down_proj`	2388
`model.layers.5.mlp.gate_proj`	2388
`model.layers.5.mlp.up_proj`	2388
`model.layers.5.self_attn.k_proj`	1638
`model.layers.5.self_attn.o_proj`	1638
`model.layers.5.self_attn.q_proj`	1638
`model.layers.5.self_attn.v_proj`	1638
`model.layers.6.mlp.down_proj`	2388
`model.layers.6.mlp.gate_proj`	2388
`model.layers.6.mlp.up_proj`	2388
`model.layers.6.self_attn.k_proj`	1638
`model.layers.6.self_attn.o_proj`	1638
`model.layers.6.self_attn.q_proj`	1638
`model.layers.6.self_attn.v_proj`	1638
`model.layers.7.mlp.down_proj`	2388
`model.layers.7.mlp.gate_proj`	2388
`model.layers.7.mlp.up_proj`	2388
`model.layers.7.self_attn.k_proj`	1638
`model.layers.7.self_attn.o_proj`	1638
`model.layers.7.self_attn.q_proj`	1638
`model.layers.7.self_attn.v_proj`	1638
`model.layers.8.mlp.down_proj`	2388
`model.layers.8.mlp.gate_proj`	2388
`model.layers.8.mlp.up_proj`	2388
`model.layers.8.self_attn.k_proj`	1638
`model.layers.8.self_attn.o_proj`	1638
`model.layers.8.self_attn.q_proj`	1638
`model.layers.8.self_attn.v_proj`	1638
`model.layers.9.mlp.down_proj`	2388
`model.layers.9.mlp.gate_proj`	2388
`model.layers.9.mlp.up_proj`	2388
`model.layers.9.self_attn.k_proj`	1638
`model.layers.9.self_attn.o_proj`	1638
`model.layers.9.self_attn.q_proj`	1638
`model.layers.9.self_attn.v_proj`	1638

Hydra Configuration Summary

Config Field	Value
Model	`unsloth/llama-2-7b`
SVD Method	`kfac_svd`
Search Method	`uniform`
Compression Target	`0.8`
Target Metric	`params`
Calibration Dataset	`wikitext2`
Calibration Samples	`256`
Sequence Length	`2048`
Seed	`233`

Downloads last month: 23

Safetensors

Model size

5B params

Tensor type

F16

Model tree for MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8

Base model

unsloth/llama-2-7b

Finetuned

(21)

this model

Dataset used to train MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8

Collection including MoritzMo123/kfac-svd_uniform_llama-2-7b_0.8

LEMS & KFAC-SVD Compression

Collection

This collection hosts the compressed models evaluated in our paper: "Layer-wise Error Modeling Search (LEMS) and KFAC-SVD". • 33 items • Updated 13 days ago • 2

Evaluation results

Perplexity on WikiText-2
test set self-reported

7.310
Perplexity on PTB
test set self-reported

53.860
Perplexity on C4
validation set self-reported

15.040
Accuracy on OpenBookQA (zero-shot)
test set self-reported

27.400
Accuracy on ARC-e (zero-shot)
test set self-reported

67.420
Accuracy on ARC-c (zero-shot)
test set self-reported

32.170
Accuracy on WinoGrande (zero-shot)
validation set self-reported

64.330
Accuracy on HellaSwag (zero-shot)
validation set self-reported

42.910