Instructions to use Lewdiculous/Pasta-Lake-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lewdiculous/Pasta-Lake-7b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Lewdiculous/Pasta-Lake-7b-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lewdiculous/Pasta-Lake-7b-GGUF", dtype="auto")

llama-cpp-python

How to use Lewdiculous/Pasta-Lake-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lewdiculous/Pasta-Lake-7b-GGUF",
	filename="Pasta-Lake-7b-IQ3_S.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Lewdiculous/Pasta-Lake-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

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 Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

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 Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Lewdiculous/Pasta-Lake-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lewdiculous/Pasta-Lake-7b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lewdiculous/Pasta-Lake-7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

SGLang

How to use Lewdiculous/Pasta-Lake-7b-GGUF 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 "Lewdiculous/Pasta-Lake-7b-GGUF" \
    --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": "Lewdiculous/Pasta-Lake-7b-GGUF",
		"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 "Lewdiculous/Pasta-Lake-7b-GGUF" \
        --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": "Lewdiculous/Pasta-Lake-7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Lewdiculous/Pasta-Lake-7b-GGUF with Ollama:
```
ollama run hf.co/Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
```

Unsloth Studio

How to use Lewdiculous/Pasta-Lake-7b-GGUF 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 Lewdiculous/Pasta-Lake-7b-GGUF 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 Lewdiculous/Pasta-Lake-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lewdiculous/Pasta-Lake-7b-GGUF to start chatting

Docker Model Runner
How to use Lewdiculous/Pasta-Lake-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M
```

Lemonade

How to use Lewdiculous/Pasta-Lake-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/Pasta-Lake-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Pasta-Lake-7b-GGUF-Q4_K_M

List all available models

lemonade list

GGUF quantizations for Pasta-Lake-7b.

If you want any specific quantization to be added, feel free to ask.

All credits belong to the creator.

SillyTavern preset files here.

Base⇢ GGUF(F16)⇢ GGUF(Quants)

The new IQ3_S merged today has shown to be better than the old Q3_K_S, so I added that instead of the later. Only supported in koboldcpp-1.60 or higher.

Using llama.cpp-b2254.

Original model information:

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Test157t/Pasta-PrimaMaid-7b
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: Test157t/Pasta-PrimaMaid-7b
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16