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
File size: 3,217 Bytes
4dffb87 f9a604c 4dffb87 f9a604c 4dffb87 f9a604c 4dffb87 e907eb4 f9a604c 4dffb87 eeec9ee a047768 eeec9ee a047768 4dffb87 f9a604c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | ---
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\n Qui va ser Isaac Newton?<|im_end|>\n<|im_start|>assistant\n"
- text: "<|im_start|>user\n ¿Quién fue Isaac Newton?<|im_end|>\n<|im_start|>assistant\n"
model-index:
- name: FLAMA-0.5-3B
results: []
---
# FLAMA: Model 3B ChatML en Català i Castellà. Versió 0.5

👉🏻 [Format GGUF i quantitzat](/xaviviro/FLAMA-0.5-3B-GGUF)
FLAMA és el primer model petit 3B bilingüe en català i castellà. És el resultat de finetunejar el model [open_llama_3b_v2](/openlm-research/open_llama_3b_v2) amb les instruccions d'[OpenAssistant v2](/datasets/OpenAssistant/oasst2) traduïdes automàticament al català i al castellà amb recursos de [Helsinki-NLP](/Helsinki-NLP) i tractades en format ChatML.
## Novetats de la versió 0.5
1. Català millorat
1. 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
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xaviviro__FLAMA-0.5-3B)
| 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|
|