modules: ['6_attn.v', '29_gate', '3_gate', '25_attn.k', '30_mlp.down', '5_attn.k', '21_mlp.down', '7_mlp.up', '23_gate', '9_mlp.up', '7_mlp.down', '10_attn.q', '11_gate', '29_attn.k', '6_mlp.down', '5_attn.v', '18_gate', '18_attn.q', '11_attn.q', '16_attn.k', '5_mlp.up', '10_attn.v', '24_attn.k', '28_mlp.up', '12_attn.v', '18_attn.k', '19_attn.o', '4_attn.o', '17_mlp.down', '28_attn.q', '26_attn.q', '28_attn.v', '10_attn.o', '24_gate', '8_gate', '21_attn.k', '24_mlp.down', '27_mlp.down', '6_gate', '12_mlp.up', '24_attn.v', '27_attn.v', '30_attn.v', '11_attn.k', '5_attn.o']

tags: ['model: vicuna', 'repo_name: vicuna_channel_0_electrical_engineering_Complete Random', 'file_name: vicuna_channel_0_electrical_engineering_Complete Random_5000_5.pt', 'base_model: lmsys/vicuna-7b-v1.5', 'pruning_style: channel', 'community: 0', 'pruning_ratio: 20', 'dataset_label: electrical_engineering', 'sparsity_ratio: 20', "dataset: ['tasksource/mmlu', 'electrical_engineering']", 'finetune: Complete Random', 'modules_size: 45', "modules: ['6_attn.v', '29_gate', '3_gate', '25_attn.k', '30_mlp.down', '5_attn.k', '21_mlp.down', '7_mlp.up', '23_gate', '9_mlp.up', '7_mlp.down', '10_attn.q', '11_gate', '29_attn.k', '6_mlp.down', '5_attn.v', '18_gate', '18_attn.q', '11_attn.q', '16_attn.k', '5_mlp.up', '10_attn.v', '24_attn.k', '28_mlp.up', '12_attn.v', '18_attn.k', '19_attn.o', '4_attn.o', '17_mlp.down', '28_attn.q', '26_attn.q', '28_attn.v', '10_attn.o', '24_gate', '8_gate', '21_attn.k', '24_mlp.down', '27_mlp.down', '6_gate', '12_mlp.up', '24_attn.v', '27_attn.v', '30_attn.v', '11_attn.k', '5_attn.o']", 'rank: 2']

Model card Files Files and versions

xet

Community

Instructions to use KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random with Transformers:

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

pipe = pipeline("text-generation", model="KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random")
model = AutoModelForCausalLM.from_pretrained("KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KBhandari11/vicuna_channel_0_electrical_engineering_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/vicuna_channel_0_electrical_engineering_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random

SGLang

How to use KBhandari11/vicuna_channel_0_electrical_engineering_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/vicuna_channel_0_electrical_engineering_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/vicuna_channel_0_electrical_engineering_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/vicuna_channel_0_electrical_engineering_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/vicuna_channel_0_electrical_engineering_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random with Docker Model Runner:
```
docker model run hf.co/KBhandari11/vicuna_channel_0_electrical_engineering_Complete_Random
```

vicuna_channel_0_electrical_engineering_Complete_Random

27 GB

Ctrl+K

1 contributor

History: 2 commits

KBhandari11

Upload LlamaForCausalLM

1256620 verified 12 months ago