Text Generation
Transformers
Safetensors
Portuguese
llama
portuguese
european-portuguese
fp8
vllm
compressed-tensors
quantized
conversational
text-generation-inference
Instructions to use CYBERS3C/AMALIA-9B-0626-DPO-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CYBERS3C/AMALIA-9B-0626-DPO-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CYBERS3C/AMALIA-9B-0626-DPO-FP8") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CYBERS3C/AMALIA-9B-0626-DPO-FP8") model = AutoModelForCausalLM.from_pretrained("CYBERS3C/AMALIA-9B-0626-DPO-FP8", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CYBERS3C/AMALIA-9B-0626-DPO-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CYBERS3C/AMALIA-9B-0626-DPO-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CYBERS3C/AMALIA-9B-0626-DPO-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CYBERS3C/AMALIA-9B-0626-DPO-FP8
- SGLang
How to use CYBERS3C/AMALIA-9B-0626-DPO-FP8 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 "CYBERS3C/AMALIA-9B-0626-DPO-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CYBERS3C/AMALIA-9B-0626-DPO-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "CYBERS3C/AMALIA-9B-0626-DPO-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CYBERS3C/AMALIA-9B-0626-DPO-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CYBERS3C/AMALIA-9B-0626-DPO-FP8 with Docker Model Runner:
docker model run hf.co/CYBERS3C/AMALIA-9B-0626-DPO-FP8
Download chat_template.jinja from CYBERS3C/AMALIA-9B-0626-DPO-FP8: direct link, hf CLI and curl.
- Browser
- Download file 625 Bytes
-
https://huggingface.co/CYBERS3C/AMALIA-9B-0626-DPO-FP8/resolve/main/chat_template.jinja
- Command line
-
hf download hf://CYBERS3C/AMALIA-9B-0626-DPO-FP8/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/CYBERS3C/AMALIA-9B-0626-DPO-FP8/resolve/main/chat_template.jinja
625 Bytes
| {% if messages[0]['role'] != 'system' %}{% set messages = [{'role': 'system', 'content': 'O teu nome é Amália, e és um modelo avançado de linguagem útil. Responde sempre na língua do utilizador, a menos que sejas instruído em contrário, e lembra-te que a tua língua principal é o português europeu.'}] + messages %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{ '<|im_start|>' + message['role'] + ' | |
| ' + message['content'] + '<|im_end|> | |
| ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant | |
| ' }}{% endif %} |