Text Generation
Transformers
Safetensors
llama
model: llama
repo_name: llama_channel_3_social_support_Community
file_name: llama_channel_3_social_support_Community_5000_5.pt
pruning_style: channel
community: 3
pruning_ratio: 20
dataset_label: social_support
sparsity_ratio: 20
['tasksource/bigbench', 'social_support']
finetune: Community
modules_size: 30
modules: ['10_attn.v', '10_gate', '10_mlp.up', '11_attn.k', '11_mlp.up', '12_attn.q', '12_attn.v', '12_mlp.down', '17_attn.q', '18_attn.o', '18_mlp.up', '19_attn.k', '19_attn.q', '19_mlp.up', '20_attn.k', '20_gate', '22_mlp.up', '23_attn.k', '24_gate', '25_mlp.down', '26_mlp.down', '28_attn.o', '30_attn.q', '30_attn.v', '3_attn.k', '3_attn.v', '4_attn.q', '5_gate', '6_attn.v', '8_mlp.down']
rank: 2
tags: ['model: llama', 'repo_name: llama_channel_3_social_support_Community', 'file_name: llama_channel_3_social_support_Community_5000_5.pt', 'base_model: meta-llama/Llama-2-7b-hf', 'pruning_style: channel', 'community: 3', 'pruning_ratio: 20', 'dataset_label: social_support', 'sparsity_ratio: 20', "dataset: ['tasksource/bigbench', 'social_support']", 'finetune: Community', 'modules_size: 30', "modules: ['10_attn.v', '10_gate', '10_mlp.up', '11_attn.k', '11_mlp.up', '12_attn.q', '12_attn.v', '12_mlp.down', '17_attn.q', '18_attn.o', '18_mlp.up', '19_attn.k', '19_attn.q', '19_mlp.up', '20_attn.k', '20_gate', '22_mlp.up', '23_attn.k', '24_gate', '25_mlp.down', '26_mlp.down', '28_attn.o', '30_attn.q', '30_attn.v', '3_attn.k', '3_attn.v', '4_attn.q', '5_gate', '6_attn.v', '8_mlp.down']", 'rank: 2']
text-generation-inference
Instructions to use KBhandari11/llama_channel_3_social_support_Community with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBhandari11/llama_channel_3_social_support_Community with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KBhandari11/llama_channel_3_social_support_Community")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_channel_3_social_support_Community") model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_channel_3_social_support_Community") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use KBhandari11/llama_channel_3_social_support_Community with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KBhandari11/llama_channel_3_social_support_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_channel_3_social_support_Community", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KBhandari11/llama_channel_3_social_support_Community
- SGLang
How to use KBhandari11/llama_channel_3_social_support_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_channel_3_social_support_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_channel_3_social_support_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_channel_3_social_support_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_channel_3_social_support_Community", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KBhandari11/llama_channel_3_social_support_Community with Docker Model Runner:
docker model run hf.co/KBhandari11/llama_channel_3_social_support_Community
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