Instructions to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF")
model = AutoModelForCausalLM.from_pretrained("Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF")

llama-cpp-python

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF",
	filename="projecte-aina-flor-6.3b-Q2_K.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 Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Christ0pher/Projecte-Aina-FLOR-6.3B-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 Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Christ0pher/Projecte-Aina-FLOR-6.3B-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 Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Christ0pher/Projecte-Aina-FLOR-6.3B-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 Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M

SGLang

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-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 "Christ0pher/Projecte-Aina-FLOR-6.3B-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": "Christ0pher/Projecte-Aina-FLOR-6.3B-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 "Christ0pher/Projecte-Aina-FLOR-6.3B-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": "Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with Ollama:
```
ollama run hf.co/Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-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 Christ0pher/Projecte-Aina-FLOR-6.3B-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 Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF to start chatting

Docker Model Runner
How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M
```

Lemonade

How to use Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Projecte-Aina-FLOR-6.3B-GGUF-Q4_K_M

List all available models

lemonade list

Projecte AINA - FLOR-6.3B - GGUF

Model creator: Projecte AINA
Original model: FLOR-6.3B

Description

This repo contains GGUF format model files for Projecte AINA's FLOR-6.3B.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

Downloads last month: 69

GGUF

Model size

6B params

Architecture

bloom

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

32-bit

Model tree for Christ0pher/Projecte-Aina-FLOR-6.3B-GGUF

Base model

projecte-aina/FLOR-6.3B

Quantized

(5)

this model