Instructions to use cstr/splade-pp-en-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/splade-pp-en-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/splade-pp-en-v1-GGUF",
	filename="splade-pp-en-v1-q4_k.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 cstr/splade-pp-en-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/splade-pp-en-v1-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/splade-pp-en-v1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/splade-pp-en-v1-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/splade-pp-en-v1-GGUF:Q8_0

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 cstr/splade-pp-en-v1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf cstr/splade-pp-en-v1-GGUF:Q8_0

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 cstr/splade-pp-en-v1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/splade-pp-en-v1-GGUF:Q8_0

Use Docker

docker model run hf.co/cstr/splade-pp-en-v1-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use cstr/splade-pp-en-v1-GGUF with Ollama:
```
ollama run hf.co/cstr/splade-pp-en-v1-GGUF:Q8_0
```

Unsloth Studio new

How to use cstr/splade-pp-en-v1-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 cstr/splade-pp-en-v1-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 cstr/splade-pp-en-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/splade-pp-en-v1-GGUF to start chatting

Docker Model Runner
How to use cstr/splade-pp-en-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/splade-pp-en-v1-GGUF:Q8_0
```

Lemonade

How to use cstr/splade-pp-en-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/splade-pp-en-v1-GGUF:Q8_0

Run and chat with the model

lemonade run user.splade-pp-en-v1-GGUF-Q8_0

List all available models

lemonade list

Splade PP en v1 GGUF

GGUF format of prithivida/Splade_PP_en_v1 for use with CrispEmbed.

SPLADE sparse embedding model for efficient keyword-based retrieval. Produces sparse term-weight vectors over the vocabulary.

Files

File	Quantization	Size
splade-pp-en-v1.gguf	F32	418 MB
splade-pp-en-v1-q8_0.gguf	Q8_0	111 MB

Quick Start

from crispembed import CrispEmbed

model = CrispEmbed("splade-pp-en-v1.gguf")
sparse = model.encode_sparse("machine learning")
# {token_id: weight} — top terms: machine(2.09), learning(1.63), ...

Credits

Original: prithivida/Splade_PP_en_v1
Engine: CrispEmbed

Downloads last month: 249

GGUF

Model size

0.1B params

Architecture

bert

Hardware compatibility

8-bit

View +1 variant

Model tree for cstr/splade-pp-en-v1-GGUF

Base model

prithivida/Splade_PP_en_v1

Quantized

(1)

this model