Text Generation
Transformers
Safetensors
llama
model: llama
repo_name: llama_block_1_language_identification_Community
file_name: llama_block_1_language_identification_Community_5000_5.pt
pruning_style: block
community: 1
pruning_ratio: 20
dataset_label: language_identification
sparsity_ratio: 20
['tasksource/bigbench', 'language_identification']
finetune: Community
modules_size: 27
modules: ['10_attn.v', '12_attn.q', '12_attn.v', '14_attn.q', '16_attn.k', '17_attn.q', '17_attn.v', '18_attn.o', '20_attn.k', '21_attn.o', '22_attn.k', '22_attn.o', '24_attn.q', '26_attn.q', '28_attn.k', '28_attn.q', '28_attn.v', '29_attn.q', '30_attn.k', '3_attn.k', '3_attn.v', '4_attn.o', '6_attn.k', '6_attn.v', '7_attn.o', '9_attn.k', '9_attn.v']
rank: 1
tags: ['model: llama', 'repo_name: llama_block_1_language_identification_Community', 'file_name: llama_block_1_language_identification_Community_5000_5.pt', 'base_model: meta-llama/Llama-2-7b-hf', 'pruning_style: block', 'community: 1', 'pruning_ratio: 20', 'dataset_label: language_identification', 'sparsity_ratio: 20', "dataset: ['tasksource/bigbench', 'language_identification']", 'finetune: Community', 'modules_size: 27', "modules: ['10_attn.v', '12_attn.q', '12_attn.v', '14_attn.q', '16_attn.k', '17_attn.q', '17_attn.v', '18_attn.o', '20_attn.k', '21_attn.o', '22_attn.k', '22_attn.o', '24_attn.q', '26_attn.q', '28_attn.k', '28_attn.q', '28_attn.v', '29_attn.q', '30_attn.k', '3_attn.k', '3_attn.v', '4_attn.o', '6_attn.k', '6_attn.v', '7_attn.o', '9_attn.k', '9_attn.v']", 'rank: 1']
text-generation-inference
Instructions to use KBhandari11/llama_block_1_language_identification_Community with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBhandari11/llama_block_1_language_identification_Community with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KBhandari11/llama_block_1_language_identification_Community")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_block_1_language_identification_Community") model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_block_1_language_identification_Community") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use KBhandari11/llama_block_1_language_identification_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_1_language_identification_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_1_language_identification_Community", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KBhandari11/llama_block_1_language_identification_Community
- SGLang
How to use KBhandari11/llama_block_1_language_identification_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_1_language_identification_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_1_language_identification_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_1_language_identification_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_1_language_identification_Community", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KBhandari11/llama_block_1_language_identification_Community with Docker Model Runner:
docker model run hf.co/KBhandari11/llama_block_1_language_identification_Community