tags: ['model: llama', 'repo_name: llama_block_4_simple_ethical_questions_Complete Random', 'file_name: llama_block_4_simple_ethical_questions_Complete Random_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: Complete Random', 'modules_size: 10', "modules: ['15_attn.o', '20_mlp.down', '14_attn.q', '22_mlp.up', '21_mlp.up', '16_attn.k', '30_mlp.up', '21_attn.o', '19_attn.k', '12_attn.v']", 'rank: 2']

Community

Instructions to use KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random 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_Complete_Random 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_Complete_Random")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random")
model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random 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_Complete_Random"
# 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_Complete_Random",
		"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_Complete_Random

SGLang

How to use KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random 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_Complete_Random" \
    --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_Complete_Random",
		"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_Complete_Random" \
        --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_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random with Docker Model Runner:
```
docker model run hf.co/KBhandari11/llama_block_4_simple_ethical_questions_Complete_Random
```

llama_block_4_simple_ethical_questions_Complete_Random

Commit History

Jun 1, 2025

11:37
Upload LlamaForCausalLM

KBhandari11

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1d1c9ce
11:31
initial commit

KBhandari11

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5bcb18a

Commit History

Jun 1, 2025

Upload LlamaForCausalLM

initial commit