xaviviro/oasst2_ca_gpt
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How to use xaviviro/FLOR-1.3B-xat with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="xaviviro/FLOR-1.3B-xat") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("xaviviro/FLOR-1.3B-xat")
model = AutoModelForCausalLM.from_pretrained("xaviviro/FLOR-1.3B-xat", device_map="auto")How to use xaviviro/FLOR-1.3B-xat with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xaviviro/FLOR-1.3B-xat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "xaviviro/FLOR-1.3B-xat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/xaviviro/FLOR-1.3B-xat
How to use xaviviro/FLOR-1.3B-xat with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "xaviviro/FLOR-1.3B-xat" \
--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": "xaviviro/FLOR-1.3B-xat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "xaviviro/FLOR-1.3B-xat" \
--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": "xaviviro/FLOR-1.3B-xat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use xaviviro/FLOR-1.3B-xat with Docker Model Runner:
docker model run hf.co/xaviviro/FLOR-1.3B-xat
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 "xaviviro/FLOR-1.3B-xat" \
--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": "xaviviro/FLOR-1.3B-xat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'FLOR-1.3B-xat és el resultat de finetunejar el model FLOR-1.3B de Projecte Aina amb les instruccions d'OpenAssistant v2 traduïdes automàticament al català amb recursos de Helsinki-NLP i tractades en format ChatML.
FLOR-1.3B-xat usa ChatML com a prompt template:
<|im_start|>user
Qui va ser Isaac Newton?<|im_end|>
<|im_start|>assistant\n
Base model
projecte-aina/FLOR-1.3B
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "xaviviro/FLOR-1.3B-xat" \ --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": "xaviviro/FLOR-1.3B-xat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'