Instructions to use cstr/jina-v5-small-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/jina-v5-small-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/jina-v5-small-GGUF",
	filename="jina-v5-small-classification.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 cstr/jina-v5-small-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/jina-v5-small-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/jina-v5-small-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/jina-v5-small-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/jina-v5-small-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/jina-v5-small-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf cstr/jina-v5-small-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/jina-v5-small-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/jina-v5-small-GGUF:Q8_0

Use Docker

docker model run hf.co/cstr/jina-v5-small-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use cstr/jina-v5-small-GGUF with Ollama:
```
ollama run hf.co/cstr/jina-v5-small-GGUF:Q8_0
```

Unsloth Studio

How to use cstr/jina-v5-small-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/jina-v5-small-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/jina-v5-small-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/jina-v5-small-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use cstr/jina-v5-small-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/jina-v5-small-GGUF:Q8_0
```

Lemonade

How to use cstr/jina-v5-small-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/jina-v5-small-GGUF:Q8_0

Run and chat with the model

lemonade run user.jina-v5-small-GGUF-Q8_0

List all available models

lemonade list

cstr commited on Apr 15

Commit

f64b401

verified ·

1 Parent(s): a06c7df

Add model card for jina-v5-small GGUF

Browse files

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -16,6 +16,7 @@ Jina Embeddings v5 Small. Full-size decoder model, 1024-dimensional.
 | File | Quantization | Size |
 |------|-------------|------|
 | [jina-v5-small-q8_0.gguf](https://huggingface.co/cstr/jina-v5-small-GGUF/resolve/main/jina-v5-small-q8_0.gguf) | Q8_0 | 609 MB |
 | [jina-v5-small.gguf](https://huggingface.co/cstr/jina-v5-small-GGUF/resolve/main/jina-v5-small.gguf) | F32 | 2279 MB |
@@ -24,10 +25,10 @@ Jina Embeddings v5 Small. Full-size decoder model, 1024-dimensional.
 ```bash
 # Download
-huggingface-cli download cstr/jina-v5-small-GGUF jina-v5-small-q8_0.gguf --local-dir .
 # Run with CrispEmbed
-./crispembed -m jina-v5-small-q8_0.gguf "Hello world"
 # Or with auto-download
 ./crispembed -m jina-v5-small "Hello world"
@@ -61,10 +62,10 @@ cd CrispEmbed
 cmake -S . -B build && cmake --build build -j
 # Encode
-./build/crispembed -m jina-v5-small-q8_0.gguf "query text"
 # Server mode
-./build/crispembed-server -m jina-v5-small-q8_0.gguf --port 8080
 curl -X POST http://localhost:8080/v1/embeddings \
     -d '{"input": ["Hello world"], "model": "jina-v5-small"}'
 ```

 | File | Quantization | Size |
 |------|-------------|------|
+| [jina-v5-small-q4_k.gguf](https://huggingface.co/cstr/jina-v5-small-GGUF/resolve/main/jina-v5-small-q4_k.gguf) | Q4_K | 835 MB |
 | [jina-v5-small-q8_0.gguf](https://huggingface.co/cstr/jina-v5-small-GGUF/resolve/main/jina-v5-small-q8_0.gguf) | Q8_0 | 609 MB |
 | [jina-v5-small.gguf](https://huggingface.co/cstr/jina-v5-small-GGUF/resolve/main/jina-v5-small.gguf) | F32 | 2279 MB |
 ```bash
 # Download
+huggingface-cli download cstr/jina-v5-small-GGUF jina-v5-small-q4_k.gguf --local-dir .
 # Run with CrispEmbed
+./crispembed -m jina-v5-small-q4_k.gguf "Hello world"
 # Or with auto-download
 ./crispembed -m jina-v5-small "Hello world"
 cmake -S . -B build && cmake --build build -j
 # Encode
+./build/crispembed -m jina-v5-small-q4_k.gguf "query text"
 # Server mode
+./build/crispembed-server -m jina-v5-small-q4_k.gguf --port 8080
 curl -X POST http://localhost:8080/v1/embeddings \
     -d '{"input": ["Hello world"], "model": "jina-v5-small"}'
 ```