modules: ['26_mlp.up', '13_attn.q', '26_attn.q', '9_attn.o', '10_mlp.down', '18_mlp.down', '27_attn.o', '16_mlp.down', '16_gate', '3_attn.v', '19_attn.v', '8_attn.k', '28_mlp.up', '29_gate', '29_attn.q', '13_mlp.up', '19_gate', '30_mlp.up', '15_gate', '25_attn.k', '11_mlp.down', '15_attn.k', '18_attn.o', '30_attn.v', '24_attn.o', '6_mlp.up', '5_gate', '22_attn.q', '29_mlp.down', '15_attn.q', '25_mlp.up', '17_attn.q', '14_gate', '7_attn.q', '27_mlp.up', '6_attn.q', '13_attn.o', '3_gate']

tags: ['model: vicuna', 'repo_name: vicuna_block_0_implicatures_Complete Random', 'file_name: vicuna_block_0_implicatures_Complete Random_5000_5.pt', 'base_model: lmsys/vicuna-7b-v1.5', 'pruning_style: block', 'community: 0', 'pruning_ratio: 20', 'dataset_label: implicatures', 'sparsity_ratio: 20', "dataset: ['tasksource/bigbench', 'implicatures']", 'finetune: Complete Random', 'modules_size: 38', "modules: ['26_mlp.up', '13_attn.q', '26_attn.q', '9_attn.o', '10_mlp.down', '18_mlp.down', '27_attn.o', '16_mlp.down', '16_gate', '3_attn.v', '19_attn.v', '8_attn.k', '28_mlp.up', '29_gate', '29_attn.q', '13_mlp.up', '19_gate', '30_mlp.up', '15_gate', '25_attn.k', '11_mlp.down', '15_attn.k', '18_attn.o', '30_attn.v', '24_attn.o', '6_mlp.up', '5_gate', '22_attn.q', '29_mlp.down', '15_attn.q', '25_mlp.up', '17_attn.q', '14_gate', '7_attn.q', '27_mlp.up', '6_attn.q', '13_attn.o', '3_gate']", 'rank: 1']

Model card Files Files and versions

xet

Community

Instructions to use KBhandari11/vicuna_block_0_implicatures_Complete_Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use KBhandari11/vicuna_block_0_implicatures_Complete_Random with Transformers:

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

pipe = pipeline("text-generation", model="KBhandari11/vicuna_block_0_implicatures_Complete_Random")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBhandari11/vicuna_block_0_implicatures_Complete_Random")
model = AutoModelForCausalLM.from_pretrained("KBhandari11/vicuna_block_0_implicatures_Complete_Random", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KBhandari11/vicuna_block_0_implicatures_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_block_0_implicatures_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_block_0_implicatures_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KBhandari11/vicuna_block_0_implicatures_Complete_Random

SGLang

How to use KBhandari11/vicuna_block_0_implicatures_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_block_0_implicatures_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_block_0_implicatures_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_block_0_implicatures_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_block_0_implicatures_Complete_Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KBhandari11/vicuna_block_0_implicatures_Complete_Random with Docker Model Runner:
```
docker model run hf.co/KBhandari11/vicuna_block_0_implicatures_Complete_Random
```

vicuna_block_0_implicatures_Complete_Random / README.md

KBhandari11

Upload LlamaForCausalLM

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	---
	library_name: transformers
	tags:
	- 'model: vicuna'
	- 'repo_name: vicuna_block_0_implicatures_Complete Random'
	- 'file_name: vicuna_block_0_implicatures_Complete Random_5000_5.pt'
	- 'base_model: lmsys/vicuna-7b-v1.5'
	- 'pruning_style: block'
	- 'community: 0'
	- 'pruning_ratio: 20'
	- 'dataset_label: implicatures'
	- 'sparsity_ratio: 20'
	- 'dataset: [''tasksource/bigbench'', ''implicatures'']'
	- 'finetune: Complete Random'
	- 'modules_size: 38'
	- 'modules: [''26_mlp.up'', ''13_attn.q'', ''26_attn.q'', ''9_attn.o'', ''10_mlp.down'',
	''18_mlp.down'', ''27_attn.o'', ''16_mlp.down'', ''16_gate'', ''3_attn.v'', ''19_attn.v'',
	''8_attn.k'', ''28_mlp.up'', ''29_gate'', ''29_attn.q'', ''13_mlp.up'', ''19_gate'',
	''30_mlp.up'', ''15_gate'', ''25_attn.k'', ''11_mlp.down'', ''15_attn.k'', ''18_attn.o'',
	''30_attn.v'', ''24_attn.o'', ''6_mlp.up'', ''5_gate'', ''22_attn.q'', ''29_mlp.down'',
	''15_attn.q'', ''25_mlp.up'', ''17_attn.q'', ''14_gate'', ''7_attn.q'', ''27_mlp.up'',
	''6_attn.q'', ''13_attn.o'', ''3_gate'']'
	- 'rank: 1'
	- 'tags: [''model: vicuna'', ''repo_name: vicuna_block_0_implicatures_Complete Random'',
	''file_name: vicuna_block_0_implicatures_Complete Random_5000_5.pt'', ''base_model:
	lmsys/vicuna-7b-v1.5'', ''pruning_style: block'', ''community: 0'', ''pruning_ratio:
	20'', ''dataset_label: implicatures'', ''sparsity_ratio: 20'', "dataset: [''tasksource/bigbench'',
	''implicatures'']", ''finetune: Complete Random'', ''modules_size: 38'', "modules:
	[''26_mlp.up'', ''13_attn.q'', ''26_attn.q'', ''9_attn.o'', ''10_mlp.down'', ''18_mlp.down'',
	''27_attn.o'', ''16_mlp.down'', ''16_gate'', ''3_attn.v'', ''19_attn.v'', ''8_attn.k'',
	''28_mlp.up'', ''29_gate'', ''29_attn.q'', ''13_mlp.up'', ''19_gate'', ''30_mlp.up'',
	''15_gate'', ''25_attn.k'', ''11_mlp.down'', ''15_attn.k'', ''18_attn.o'', ''30_attn.v'',
	''24_attn.o'', ''6_mlp.up'', ''5_gate'', ''22_attn.q'', ''29_mlp.down'', ''15_attn.q'',
	''25_mlp.up'', ''17_attn.q'', ''14_gate'', ''7_attn.q'', ''27_mlp.up'', ''6_attn.q'',
	''13_attn.o'', ''3_gate'']", ''rank: 1'']'
	---

	# Model Card for Model ID

	<!-- Provide a quick summary of what the model is/does. -->



	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->

	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

	- Developed by: [More Information Needed]
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	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

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	#### Preprocessing [optional]

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	#### Training Hyperparameters

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	## Model Examination [optional]

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	## Environmental Impact

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	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- Hardware Type: [More Information Needed]
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	## Technical Specifications [optional]

	### Model Architecture and Objective

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	## Citation [optional]

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	## Glossary [optional]

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