Text Generation
Transformers
Safetensors
llama
model: llama_chat
repo_name: llama_chat_block_2_suicide_risk_Complete Random
file_name: llama_chat_block_2_suicide_risk_Complete Random_5000_5.pt
pruning_style: block
community: 2
pruning_ratio: 20
dataset_label: suicide_risk
sparsity_ratio: 20
['tasksource/bigbench', 'suicide_risk']
finetune: Complete Random
modules_size: 33
modules: ['5_mlp.up', '5_mlp.down', '4_mlp.up', '5_attn.o', '19_attn.o', '28_mlp.down', '25_mlp.down', '29_attn.q', '27_mlp.up', '21_mlp.down', '12_attn.q', '13_attn.k', '16_gate', '6_gate', '20_attn.q', '30_attn.q', '4_attn.k', '23_attn.k', '8_attn.o', '26_gate', '20_mlp.up', '17_mlp.up', '3_mlp.up', '17_attn.k', '11_attn.v', '3_gate', '29_attn.v', '6_attn.v', '7_attn.o', '27_mlp.down', '4_attn.q', '14_mlp.down', '8_gate']
rank: 2
tags: ['model: llama_chat', 'repo_name: llama_chat_block_2_suicide_risk_Complete Random', 'file_name: llama_chat_block_2_suicide_risk_Complete Random_5000_5.pt', 'base_model: meta-llama/Llama-2-7b-chat-hf', 'pruning_style: block', 'community: 2', 'pruning_ratio: 20', 'dataset_label: suicide_risk', 'sparsity_ratio: 20', "dataset: ['tasksource/bigbench', 'suicide_risk']", 'finetune: Complete Random', 'modules_size: 33', "modules: ['5_mlp.up', '5_mlp.down', '4_mlp.up', '5_attn.o', '19_attn.o', '28_mlp.down', '25_mlp.down', '29_attn.q', '27_mlp.up', '21_mlp.down', '12_attn.q', '13_attn.k', '16_gate', '6_gate', '20_attn.q', '30_attn.q', '4_attn.k', '23_attn.k', '8_attn.o', '26_gate', '20_mlp.up', '17_mlp.up', '3_mlp.up', '17_attn.k', '11_attn.v', '3_gate', '29_attn.v', '6_attn.v', '7_attn.o', '27_mlp.down', '4_attn.q', '14_mlp.down', '8_gate']", 'rank: 2']
text-generation-inference
Instructions to use KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random") model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KBhandari11/llama_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_Complete_Random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random
- SGLang
How to use KBhandari11/llama_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_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_chat_block_2_suicide_risk_Complete_Random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random with Docker Model Runner:
docker model run hf.co/KBhandari11/llama_chat_block_2_suicide_risk_Complete_Random
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