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

d0be991

verified ·

1 Parent(s): 3bfaf33

Update README.md

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README.md CHANGED Viewed

@@ -60,22 +60,31 @@ Measured with `bench_laguna_generate` from lucebox-hub (dflash autoregressive fo
 ### lucebox-hub (dflash + PFlash, recommended for 128K)
 ```bash
 git clone https://github.com/Luce-Org/lucebox-hub
 cd lucebox-hub/dflash
-cmake -B build -DCMAKE_CUDA_ARCHITECTURES=86  # 86 for 3090, 89 for 4090, 120 for 5090
 cmake --build build -j
 hf download Lucebox/Laguna-XS.2-GGUF laguna-xs2-Q4_K_M.gguf --local-dir models/
-hf download poolside/Laguna-XS.2 chat_template.jinja tokenizer.json tokenizer_config.json special_tokens_map.json config.json --local-dir models/Laguna-XS-2
 python3 scripts/server.py \
-    --target models/laguna-xs2-Q4_K_M.gguf \
-    --tokenizer models/Laguna-XS-2 \
-    --port 8000 --max-ctx 131072
 curl http://localhost:8000/v1/chat/completions \
-    -H 'Content-Type: application/json' \
-    -d '{"model":"luce-dflash","messages":[{"role":"user","content":"hello"}],"stream":true}'
 ```
 ## License

 ### lucebox-hub (dflash + PFlash, recommended for 128K)
 ```bash
+# clone
 git clone https://github.com/Luce-Org/lucebox-hub
 cd lucebox-hub/dflash
+# build with sm_86 (3090 / A6000)
+cmake -B build -DCMAKE_CUDA_ARCHITECTURES=86
 cmake --build build -j
+# fetch the Q4_K_M GGUF + Poolside tokenizer
 hf download Lucebox/Laguna-XS.2-GGUF laguna-xs2-Q4_K_M.gguf --local-dir models/
+hf download poolside/Laguna-XS.2 chat_template.jinja tokenizer.json tokenizer_config.json \
+   special_tokens_map.json config.json --local-dir models/Laguna-XS-2
+# run the OpenAI server (same server.py as qwen35, arch auto-detected from GGUF).
+# -ctk/-ctv q4_0 keeps the 131K KV cache under ~6 GB so weights + KV fit on 24 GB.
 python3 scripts/server.py \
+  --target models/laguna-xs2-Q4_K_M.gguf \
+  --tokenizer models/Laguna-XS-2 \
+  --port 8000 --max-ctx 131072 \
+  -ctk q4_0 -ctv q4_0
+# chat
 curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model":"luce-dflash","messages":[{"role":"user","content":"hello"}],"stream":true}'
 ```
 ## License