modules: ['9_mlp.up', '16_mlp.up', '9_attn.o', '30_mlp.up', '18_mlp.down', '26_gate', '12_mlp.up', '5_gate', '19_attn.k', '11_attn.v', '15_attn.q', '25_mlp.down', '10_attn.v', '18_gate', '9_gate', '26_mlp.up', '25_mlp.up', '22_gate', '11_attn.k', '21_mlp.up', '26_attn.v', '29_attn.q', '27_attn.q', '27_gate', '28_attn.k', '8_attn.q', '13_mlp.down', '12_attn.k', '17_mlp.down', '4_attn.o', '9_attn.k', '18_attn.v', '8_attn.o', '12_attn.q', '17_attn.o', '4_attn.v', '11_attn.o', '14_attn.q', '22_attn.q', '19_mlp.down', '28_attn.v', '15_mlp.down', '30_mlp.down', '18_mlp.up', '3_attn.v', '6_mlp.up', '19_attn.o', '23_mlp.up', '19_mlp.up', '28_attn.o', '21_attn.v', '5_attn.o', '15_attn.o', '14_attn.v', '3_attn.k', '6_attn.o']

tags: ['model: llama', 'repo_name: llama_channel_2_global_facts_Complete Random', 'file_name: llama_channel_2_global_facts_Complete Random_5000_5.pt', 'base_model: meta-llama/Llama-2-7b-hf', 'pruning_style: channel', 'community: 2', 'pruning_ratio: 20', 'dataset_label: global_facts', 'sparsity_ratio: 20', "dataset: ['tasksource/mmlu', 'global_facts']", 'finetune: Complete Random', 'modules_size: 56', "modules: ['9_mlp.up', '16_mlp.up', '9_attn.o', '30_mlp.up', '18_mlp.down', '26_gate', '12_mlp.up', '5_gate', '19_attn.k', '11_attn.v', '15_attn.q', '25_mlp.down', '10_attn.v', '18_gate', '9_gate', '26_mlp.up', '25_mlp.up', '22_gate', '11_attn.k', '21_mlp.up', '26_attn.v', '29_attn.q', '27_attn.q', '27_gate', '28_attn.k', '8_attn.q', '13_mlp.down', '12_attn.k', '17_mlp.down', '4_attn.o', '9_attn.k', '18_attn.v', '8_attn.o', '12_attn.q', '17_attn.o', '4_attn.v', '11_attn.o', '14_attn.q', '22_attn.q', '19_mlp.down', '28_attn.v', '15_mlp.down', '30_mlp.down', '18_mlp.up', '3_attn.v', '6_mlp.up', '19_attn.o', '23_mlp.up', '19_mlp.up', '28_attn.o', '21_attn.v', '5_attn.o', '15_attn.o', '14_attn.v', '3_attn.k', '6_attn.o']", 'rank: 2']

Model card Files Files and versions

xet

Community

Instructions to use KBhandari11/llama_channel_2_global_facts_Complete_Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use KBhandari11/llama_channel_2_global_facts_Complete_Random with Transformers:

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

pipe = pipeline("text-generation", model="KBhandari11/llama_channel_2_global_facts_Complete_Random")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBhandari11/llama_channel_2_global_facts_Complete_Random")
model = AutoModelForCausalLM.from_pretrained("KBhandari11/llama_channel_2_global_facts_Complete_Random", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KBhandari11/llama_channel_2_global_facts_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_channel_2_global_facts_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_channel_2_global_facts_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KBhandari11/llama_channel_2_global_facts_Complete_Random

SGLang

How to use KBhandari11/llama_channel_2_global_facts_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_channel_2_global_facts_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_channel_2_global_facts_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_channel_2_global_facts_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_channel_2_global_facts_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KBhandari11/llama_channel_2_global_facts_Complete_Random with Docker Model Runner:
```
docker model run hf.co/KBhandari11/llama_channel_2_global_facts_Complete_Random
```

llama_channel_2_global_facts_Complete_Random

27 GB

Ctrl+K

1 contributor

History: 2 commits

KBhandari11

Upload LlamaForCausalLM

022e5da verified about 1 year ago