Text Generation
Transformers
Safetensors
English
Spanish
Catalan
bloom
FLOR
spanish
catalan
english
text-generation-inference
Instructions to use projecte-aina/FLOR-6.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/FLOR-6.3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="projecte-aina/FLOR-6.3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("projecte-aina/FLOR-6.3B") model = AutoModelForCausalLM.from_pretrained("projecte-aina/FLOR-6.3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use projecte-aina/FLOR-6.3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "projecte-aina/FLOR-6.3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "projecte-aina/FLOR-6.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/projecte-aina/FLOR-6.3B
- SGLang
How to use projecte-aina/FLOR-6.3B 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 "projecte-aina/FLOR-6.3B" \ --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": "projecte-aina/FLOR-6.3B", "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 "projecte-aina/FLOR-6.3B" \ --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": "projecte-aina/FLOR-6.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use projecte-aina/FLOR-6.3B with Docker Model Runner:
docker model run hf.co/projecte-aina/FLOR-6.3B
Effects of quantization in Bloom models
#8
by elsatch - opened
Hi!
I have been trying the Flor model, following the instructions on the model card and performs ok. I have also tried quantizing it to Q4_0 and the output goes wild. Compared to other models like Mistral, the quantized results don't seem to be on par with the unquantized version.
Have you researched about the quantization effect in some models like BLOOM?
Thanks in advance!