Instructions to use vihangd/shearedplats-2.7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vihangd/shearedplats-2.7b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="vihangd/shearedplats-2.7b-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vihangd/shearedplats-2.7b-v2")
model = AutoModelForCausalLM.from_pretrained("vihangd/shearedplats-2.7b-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vihangd/shearedplats-2.7b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vihangd/shearedplats-2.7b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vihangd/shearedplats-2.7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vihangd/shearedplats-2.7b-v2

SGLang

How to use vihangd/shearedplats-2.7b-v2 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 "vihangd/shearedplats-2.7b-v2" \
    --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": "vihangd/shearedplats-2.7b-v2",
		"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 "vihangd/shearedplats-2.7b-v2" \
        --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": "vihangd/shearedplats-2.7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vihangd/shearedplats-2.7b-v2 with Docker Model Runner:
```
docker model run hf.co/vihangd/shearedplats-2.7b-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ShearedPlats-7b

An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA (version 2)

Datasets

Trained on alpca style datasets

Prompt Template

Uses alpaca style prompt template

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.72
ARC (25-shot)	42.41
HellaSwag (10-shot)	72.58
MMLU (5-shot)	27.52
TruthfulQA (0-shot)	39.76
Winogrande (5-shot)	65.9
GSM8K (5-shot)	1.52
DROP (3-shot)	7.34

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