tags: ['model: llama', 'repo_name: llama_block_4_simple_ethical_questions_Community', 'file_name: llama_block_4_simple_ethical_questions_Community_5000_5.pt', 'base_model: meta-llama/Llama-2-7b-hf', 'pruning_style: block', 'community: 4', 'pruning_ratio: 20', 'dataset_label: simple_ethical_questions', 'sparsity_ratio: 20', "dataset: ['tasksource/bigbench', 'simple_ethical_questions']", 'finetune: Community', 'modules_size: 10', "modules: ['10_attn.q', '13_attn.k', '15_attn.k', '16_attn.v', '18_attn.q', '21_attn.v', '24_attn.o', '29_attn.o', '6_attn.q', '7_attn.v']", 'rank: 2']

Community

Instructions to use KBhandari11/llama_block_4_simple_ethical_questions_Community with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use KBhandari11/llama_block_4_simple_ethical_questions_Community with Transformers:

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

pipe = pipeline("text-generation", model="KBhandari11/llama_block_4_simple_ethical_questions_Community")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_block_4_simple_ethical_questions_Community")
model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_block_4_simple_ethical_questions_Community", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KBhandari11/llama_block_4_simple_ethical_questions_Community with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KBhandari11/llama_block_4_simple_ethical_questions_Community"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KBhandari11/llama_block_4_simple_ethical_questions_Community",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KBhandari11/llama_block_4_simple_ethical_questions_Community

SGLang

How to use KBhandari11/llama_block_4_simple_ethical_questions_Community 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 "KBhandari11/llama_block_4_simple_ethical_questions_Community" \
    --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": "KBhandari11/llama_block_4_simple_ethical_questions_Community",
		"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 "KBhandari11/llama_block_4_simple_ethical_questions_Community" \
        --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": "KBhandari11/llama_block_4_simple_ethical_questions_Community",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KBhandari11/llama_block_4_simple_ethical_questions_Community with Docker Model Runner:
```
docker model run hf.co/KBhandari11/llama_block_4_simple_ethical_questions_Community
```

llama_block_4_simple_ethical_questions_Community

Commit History

Upload LlamaForCausalLM

ece8aa7
verified

KBhandari11 commited on Jun 1, 2025

initial commit

f6fa906
verified

KBhandari11 commited on Jun 1, 2025

Commit History

Upload LlamaForCausalLM ece8aa7 verified

initial commit f6fa906 verified

Upload LlamaForCausalLM

ece8aa7
verified

initial commit

f6fa906
verified