README.md

---
license: llama3
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- catallama/CataLlama-v0.1-Base
tags:
- llama
- llama-3
- Catalan
model-index:
- name: CataLlama-v0.2-Base
  results: []
language:
- ca
- en
pipeline_tag: text-generation
library_name: transformers

---

## Model Details

**CataLlama-v0.2-Base** is a merge between [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3-8B-Instruct) and [catallama/CataLlama-v0.1-Base](https://huggingface.co./catallama/CataLlama-v0.1-Base)

The resulting model retained the Catalan language skills of CataLlama-v0.1-Base, while acquiring basic skills in instruction following.

**This is a base model and it is not fine-tuned for downstream tasks** although it has acquired some instruction following skills after the merge.

**Model developers** [Laurentiu Petrea](https://www.linkedin.com/in/laurentiupetrea/).

**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:

```python
import transformers
import torch

model_id = "catallama/CataLlama-v0.2-Base"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

outputs = pipeline("Ei com estàs avui?")

print(outputs[0]["generated_text"][len(prompt):])
```


## Merging procedure

The merge was performed only between the 32 layers of the two models, excluding the embedding, norm and the head layers.

The weights of the 32 layers were merged in a 2/3 proportion of CataLlama-v0.1-Base and 1/3 proportion of Meta-Llama-3-8B-Instruct.

The embedding, norm and head layers are copied from Meta-Llama-3-8B-Instruct without modification.

This was done with a custom script, **without** mergekit.

## 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.