Instructions to use Aryanne/sheared-silicon10p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aryanne/sheared-silicon10p with Transformers:

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

pipe = pipeline("text-generation", model="Aryanne/sheared-silicon10p")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aryanne/sheared-silicon10p")
model = AutoModelForCausalLM.from_pretrained("Aryanne/sheared-silicon10p")

llama-cpp-python

How to use Aryanne/sheared-silicon10p with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Aryanne/sheared-silicon10p",
	filename="f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Aryanne/sheared-silicon10p with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Aryanne/sheared-silicon10p:F16
# Run inference directly in the terminal:
llama-cli -hf Aryanne/sheared-silicon10p:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Aryanne/sheared-silicon10p:F16
# Run inference directly in the terminal:
llama-cli -hf Aryanne/sheared-silicon10p:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Aryanne/sheared-silicon10p:F16
# Run inference directly in the terminal:
./llama-cli -hf Aryanne/sheared-silicon10p:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Aryanne/sheared-silicon10p:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aryanne/sheared-silicon10p:F16

Use Docker

docker model run hf.co/Aryanne/sheared-silicon10p:F16

LM Studio
Jan

vLLM

How to use Aryanne/sheared-silicon10p with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aryanne/sheared-silicon10p"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aryanne/sheared-silicon10p",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Aryanne/sheared-silicon10p:F16

SGLang

How to use Aryanne/sheared-silicon10p 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 "Aryanne/sheared-silicon10p" \
    --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": "Aryanne/sheared-silicon10p",
		"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 "Aryanne/sheared-silicon10p" \
        --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": "Aryanne/sheared-silicon10p",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Aryanne/sheared-silicon10p with Ollama:
```
ollama run hf.co/Aryanne/sheared-silicon10p:F16
```

Unsloth Studio new

How to use Aryanne/sheared-silicon10p with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aryanne/sheared-silicon10p to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aryanne/sheared-silicon10p to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aryanne/sheared-silicon10p to start chatting

Docker Model Runner
How to use Aryanne/sheared-silicon10p with Docker Model Runner:
```
docker model run hf.co/Aryanne/sheared-silicon10p:F16
```

Lemonade

How to use Aryanne/sheared-silicon10p with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aryanne/sheared-silicon10p:F16

Run and chat with the model

lemonade run user.sheared-silicon10p-F16

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Testing-only don't download

Another trial of merging models with different sizes, still under testing, it's unstable at the moment.

Recipe:

merge_method: task_anysize
base_model: princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT
models:
  - model: SanjiWatsuki/Silicon-Maid-7B
    parameters:
      weight: 0.1
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	35.82
AI2 Reasoning Challenge (25-Shot)	36.18
HellaSwag (10-Shot)	51.12
MMLU (5-Shot)	25.56
TruthfulQA (0-shot)	44.85
Winogrande (5-shot)	57.22
GSM8k (5-shot)	0.00

Downloads last month: 226

Safetensors

Model size

3B params

Tensor type

F16

Model tree for Aryanne/sheared-silicon10p

Quantizations

2 models

Collection including Aryanne/sheared-silicon10p

Testing Might be broken

Collection

testing only models, • 8 items • Updated Mar 2 • 2

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

36.180
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

51.120
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

25.560
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

44.850
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

57.220
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000