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---
license: llama3
base_model: catallama/CataLlama-v0.2-Base
tags:
- llama
- llama-3
- Catalan
model-index:
- name: CataLlama-v0.2-Instruct-SFT
results: []
datasets:
- catallama/Catalan-Instruct-V2
language:
- ca
- en
pipeline_tag: text-generation
library_name: transformers
---
# CataLlama-v0.2-Instruct-SFT
**CataLlama-v0.2-Instruct-SFT** is an instruct fine-tune of [catallama/CataLlama-v0.2-Base](https://huggingface.co./catallama/CataLlama-v0.2-Base) on the [catallama/Catalan-Instruct-V2](https://huggingface.co./datasets/catallama/Catalan-Instruct-V2) dataset.
CataLlama-v0.2 was trained on roughly **800 million new tokens** which is almost double compared to CataLlama-v0.1.
The model shows improved proficiency with the Catalan language while performing significantly better than CataLlama-v0.2 on all tasks.
**This is an instruction fine-tuned model proficient on the following tasks in Catalan**
- *Information extraction (suitable for RAG)*
- *Named Entity Recognition (NER)*
- *Translation from English to Catalan and Catalan to English*
- *Summarization - both short form and long form*
- *Sentiment analysis*
**Model developers** [Laurentiu Petrea](https://www.linkedin.com/in/laurentiupetrea/) based on Llama-3 from Meta.
**Model Architecture** CataLlama is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and direct preference optimisation (DPO) to align with human preferences for helpfulness and safety.
**License** The model uses the llama-3 license available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
### Use with transformers
See the snippet below for usage with Transformers:
**The model follows the same prompt template as Llama-3 Instruct**
```python
import transformers
import torch
model_id = "catallama/CataLlama-v0.2-Instruct-SFT"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "user", "content": "Ei com estàs avui?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
outputs = pipeline(
prompt,
max_new_tokens=1024,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## Training procedure
The model was trained **with the same prompt template of Llama-3 Instruct**.
The model was trained for two epochs on **8x A100 80GB GPUs using DeepSpeed ZeRO** State-3 without CPU offloading.
Then training lasted approximately 8 hours for a total GPU cost of 150€.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- distributed_type: multi-GPU
- num_devices: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
## Intended Use
**Note:** This model is not intended to beat benchmarks, but to demonstrate techniques for augmenting LLMs on new languages and preserve rare languages as part of our world heritage.
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. |