Instructions to use cibernicola/FLOR-6.3B-xat-Q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cibernicola/FLOR-6.3B-xat-Q8_0 with Transformers:

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

pipe = pipeline("text-generation", model="cibernicola/FLOR-6.3B-xat-Q8_0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cibernicola/FLOR-6.3B-xat-Q8_0")
model = AutoModelForCausalLM.from_pretrained("cibernicola/FLOR-6.3B-xat-Q8_0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cibernicola/FLOR-6.3B-xat-Q8_0 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf cibernicola/FLOR-6.3B-xat-Q8_0: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 cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf cibernicola/FLOR-6.3B-xat-Q8_0: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 cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0

Use Docker

docker model run hf.co/cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0

LM Studio
Jan

vLLM

How to use cibernicola/FLOR-6.3B-xat-Q8_0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cibernicola/FLOR-6.3B-xat-Q8_0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cibernicola/FLOR-6.3B-xat-Q8_0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0

SGLang

How to use cibernicola/FLOR-6.3B-xat-Q8_0 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 "cibernicola/FLOR-6.3B-xat-Q8_0" \
    --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": "cibernicola/FLOR-6.3B-xat-Q8_0",
		"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 "cibernicola/FLOR-6.3B-xat-Q8_0" \
        --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": "cibernicola/FLOR-6.3B-xat-Q8_0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use cibernicola/FLOR-6.3B-xat-Q8_0 with Ollama:
```
ollama run hf.co/cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
```

Unsloth Studio

How to use cibernicola/FLOR-6.3B-xat-Q8_0 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 cibernicola/FLOR-6.3B-xat-Q8_0 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 cibernicola/FLOR-6.3B-xat-Q8_0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cibernicola/FLOR-6.3B-xat-Q8_0 to start chatting

Docker Model Runner
How to use cibernicola/FLOR-6.3B-xat-Q8_0 with Docker Model Runner:
```
docker model run hf.co/cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0
```

Lemonade

How to use cibernicola/FLOR-6.3B-xat-Q8_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cibernicola/FLOR-6.3B-xat-Q8_0:Q8_0

Run and chat with the model

lemonade run user.FLOR-6.3B-xat-Q8_0-Q8_0

List all available models

lemonade list

Atomic Chat

FLOR-6.3B-xat-Q8_0

FLOR-6.3B-xat-Q8_0 és la versió quantitzada del model FLOR-6.3B-xat d'en xaviviro

Prompt Template

FLOR-6.3B-xat-Q8_0 usa ChatML com a prompt template:

<|im_start|>user
Qui va ser Isaac Newton?<|im_end|>
<|im_start|>assistant\n

Downloads last month: 45

GGUF

Model size

6B params

Architecture

bloom

Hardware compatibility

8-bit

Model tree for cibernicola/FLOR-6.3B-xat-Q8_0

Base model

projecte-aina/FLOR-6.3B

Quantized

(5)

this model

cibernicola
/

FLOR-6.3B-xat-Q8_0

FLOR-6.3B-xat-Q8_0

Prompt Template

Model tree for cibernicola/FLOR-6.3B-xat-Q8_0

Dataset used to train cibernicola/FLOR-6.3B-xat-Q8_0