Text Generation
Transformers
PyTorch
Catalan
Spanish
English
llama
finetune
chatml
gpt4
catalan
text-generation-inference
Instructions to use xaviviro/FLAMA-0.5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xaviviro/FLAMA-0.5-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xaviviro/FLAMA-0.5-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("xaviviro/FLAMA-0.5-3B") model = AutoModelForCausalLM.from_pretrained("xaviviro/FLAMA-0.5-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xaviviro/FLAMA-0.5-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xaviviro/FLAMA-0.5-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xaviviro/FLAMA-0.5-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xaviviro/FLAMA-0.5-3B
- SGLang
How to use xaviviro/FLAMA-0.5-3B 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 "xaviviro/FLAMA-0.5-3B" \ --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": "xaviviro/FLAMA-0.5-3B", "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 "xaviviro/FLAMA-0.5-3B" \ --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": "xaviviro/FLAMA-0.5-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xaviviro/FLAMA-0.5-3B with Docker Model Runner:
docker model run hf.co/xaviviro/FLAMA-0.5-3B
metadata
language:
- ca
- es
- en
license: apache-2.0
library_name: transformers
tags:
- finetune
- chatml
- gpt4
- catalan
datasets:
- xaviviro/oasst2_ca_gpt
- xaviviro/oasst2_es_gpt
base_model: openlm-research/open_llama_3b_v2
widget:
- text: |
<|im_start|>user
Qui va ser Isaac Newton?<|im_end|>
<|im_start|>assistant
- text: |
<|im_start|>user
¿Quién fue Isaac Newton?<|im_end|>
<|im_start|>assistant
model-index:
- name: FLAMA-0.5-3B
results: []
FLAMA: Model 3B ChatML en Català i Castellà. Versió 0.5
FLAMA és el primer model petit 3B bilingüe en català i castellà. És el resultat de finetunejar el model open_llama_3b_v2 amb les instruccions d'OpenAssistant v2 traduïdes automàticament al català i al castellà amb recursos de Helsinki-NLP i tractades en format ChatML.
Novetats de la versió 0.5
- Català millorat
- Afegit el Castellà
Prompt Template
FLAMA usa ChatML com a prompt template:
<|im_start|>user
Qui va ser Isaac Newton?<|im_end|>
<|im_start|>assistant\n
<|im_start|>user
Quien fué Isaac Newton?<|im_end|>
<|im_start|>assistant\n
Referències
@software{xaviviro2023flama,
author = {xaviviro},
title = {FLAMA: Model 3B ChatML en Català. Versió 0.5},
month = January,
year = 2024,
url = {https://huggingface.co/xaviviro/FLAMA-0.5-3B}
}
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 39.23 |
| AI2 Reasoning Challenge (25-Shot) | 37.97 |
| HellaSwag (10-Shot) | 67.65 |
| MMLU (5-Shot) | 25.73 |
| TruthfulQA (0-shot) | 41.11 |
| Winogrande (5-shot) | 62.12 |
| GSM8k (5-shot) | 0.83 |
