Instructions to use namannn/llama2-13b-hyperbolic-cluster-pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use namannn/llama2-13b-hyperbolic-cluster-pruned with Transformers:

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

pipe = pipeline("text-generation", model="namannn/llama2-13b-hyperbolic-cluster-pruned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("namannn/llama2-13b-hyperbolic-cluster-pruned")
model = AutoModelForCausalLM.from_pretrained("namannn/llama2-13b-hyperbolic-cluster-pruned")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use namannn/llama2-13b-hyperbolic-cluster-pruned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "namannn/llama2-13b-hyperbolic-cluster-pruned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "namannn/llama2-13b-hyperbolic-cluster-pruned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/namannn/llama2-13b-hyperbolic-cluster-pruned

SGLang

How to use namannn/llama2-13b-hyperbolic-cluster-pruned 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 "namannn/llama2-13b-hyperbolic-cluster-pruned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "namannn/llama2-13b-hyperbolic-cluster-pruned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "namannn/llama2-13b-hyperbolic-cluster-pruned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "namannn/llama2-13b-hyperbolic-cluster-pruned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use namannn/llama2-13b-hyperbolic-cluster-pruned with Docker Model Runner:
```
docker model run hf.co/namannn/llama2-13b-hyperbolic-cluster-pruned
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merged LLaMA Model

This is a merged version of the LLaMA2-13b model based on hyperboloid projections. The model retains 31 layers with significant performance retention across all benchmarks.

Downloads last month: 4

Safetensors

Model size

10B params

Tensor type

F16

Model tree for namannn/llama2-13b-hyperbolic-cluster-pruned

Base model

meta-llama/Llama-2-13b

Finetuned

(6)

this model

Quantizations

1 model

Space using namannn/llama2-13b-hyperbolic-cluster-pruned 1

Evaluation results

Accuracy on HellaSwag
OpenCompass

66.660
Accuracy on PIQA
OpenCompass

72.140
Perplexity on WSC
OpenCompass

60.380
Accuracy on WSC
OpenCompass

38.990
Accuracy on CSQA
OpenCompass

54.360
Accuracy on MMLU
OpenCompass

54.760
Accuracy on RACE
OpenCompass

53.890
Accuracy on RACE
OpenCompass

55.290