Instructions to use Lucebox/Laguna-XS.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lucebox/Laguna-XS.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lucebox/Laguna-XS.2-GGUF",
	filename="laguna-xs2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Lucebox/Laguna-XS.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Lucebox/Laguna-XS.2-GGUF with Ollama:
```
ollama run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lucebox/Laguna-XS.2-GGUF to start chatting

Docker Model Runner
How to use Lucebox/Laguna-XS.2-GGUF with Docker Model Runner:
```
docker model run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M
```

Lemonade

How to use Lucebox/Laguna-XS.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lucebox/Laguna-XS.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Laguna-XS.2-GGUF-Q4_K_M

List all available models

lemonade list

davide221 commited on 18 days ago

Commit

3bfaf33

verified ·

1 Parent(s): 34bf60a

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -12

README.md CHANGED Viewed

@@ -51,13 +51,10 @@ Measured with `bench_laguna_generate` from lucebox-hub (dflash autoregressive fo
 |----------|-----------|-------|
 | Decode @ ctx=128 (greedy) | **113 tok/s** | n_gen=128 |
 | Decode @ ctx=1K | 104 tok/s | |
-| Decode @ ctx=4K | 60 tok/s | |
-| llama.cpp tg128 (Q8_0 KV, FA on) | 165 tok/s | for comparison |
 | 128K TTFT via dflash + PFlash | **15.91 s** | 5.4× faster than llama.cpp pp131072 (86.60 s) |
 | Loader VRAM | 18.77 GiB | + 110 MiB tok_embd kept on CPU |
-A100 SXM Q4_K_M: ~155 tok/s decode (single user, short ctx).
 ## Usage
 ### lucebox-hub (dflash + PFlash, recommended for 128K)
@@ -81,14 +78,6 @@ curl http://localhost:8000/v1/chat/completions \
     -d '{"model":"luce-dflash","messages":[{"role":"user","content":"hello"}],"stream":true}'
 ```
-### llama.cpp
-Requires llama.cpp with `laguna` arch support. The lucebox-hub fork at `dflash/deps/llama.cpp` adds it; upstream PR pending.
-```bash
-./llama-bench -m laguna-xs2-Q4_K_M.gguf -p 0 -n 128 -ctk q8_0 -ctv q8_0 -fa 1 -ngl 99
-```
 ## License
 Apache 2.0, inherited from upstream `poolside/Laguna-XS.2`.

 |----------|-----------|-------|
 | Decode @ ctx=128 (greedy) | **113 tok/s** | n_gen=128 |
 | Decode @ ctx=1K | 104 tok/s | |
+| Decode @ ctx=4K | 65 tok/s | |
 | 128K TTFT via dflash + PFlash | **15.91 s** | 5.4× faster than llama.cpp pp131072 (86.60 s) |
 | Loader VRAM | 18.77 GiB | + 110 MiB tok_embd kept on CPU |
 ## Usage
 ### lucebox-hub (dflash + PFlash, recommended for 128K)
     -d '{"model":"luce-dflash","messages":[{"role":"user","content":"hello"}],"stream":true}'
 ```
 ## License
 Apache 2.0, inherited from upstream `poolside/Laguna-XS.2`.