README.md

---
license: llama3
base_model: catallama/CataLlama-v0.1-Instruct-SFT
tags:
- llama
- llama-3
- Catalan
model-index:
- name: catallama/CataLlama-v0.1-Instruct-DPO
  results: []
datasets:
- catallama/Catalan-DPO
language:
- ca
- en
pipeline_tag: text-generation
---

# NOTE: [CataLlama-v0.2](https://huggingface.co./catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged) is out. Please use that one instead.


![](https://huggingface.co./catallama/CataLlama-v0.1-Instruct-DPO/resolve/main/CataLlama-v0.1.png)


# NOTE: [CataLlama-v0.2](https://huggingface.co./catallama/CataLlama-v0.2-Instruct-SFT-DPO-Merged) is out. Please use that one instead.


# CataLlama-v0.1-Instruct-DPO

**CataLlama-v0.1-Instruct-DPO** is a DPO fine-tune of [catallama/CataLlama-v0.1-Instruct-SFT](https://huggingface.co./catallama/CataLlama-v0.1-Instruct-SFT) on the [catallama/Catalan-DPO](https://huggingface.co./datasets/catallama/Catalan-DPO) dataset.

CataLlama was trained on roughly **445 million new tokens** in three separate stages. This is the 3rd and final stage of the training.

The model shows improved proficiency with the Catalan language.

**This is an instruction fine-tuned model, optimised with DPO, 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)


## Benchmarks

| Benchmark          | Value  |
| ------------------ | ------ |
| MMLU 5 shot        | 47.34  |
| GSM8K cot 8 shot   | 43.29  |


### 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-v0.1-Instruct-DPO"

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):])
```

**Actual Inference Examples**

```
Prompt: Pot Donald Trump sopar amb Juli Cèsar?

Answer: No, Donald Trump no podria sopar amb Juli Cèsar, ja que Juli Cèsar va morir fa més de 2.000 anys i, per tant, no està viu per a poder sopar amb ell. A més, Juli Cèsar va ser un personatge històric i polític de l'antiga Roma, mentre que Donald Trump és el 45è president dels Estats Units, personatges que pertanyen a dues èpoques i cultures diferents. Per tant, la idea de que poguessin sopar junts és absurda i no té cap sentit històric ni lògic.
```

```
Prompt: A quina velocitat poden volar els cocodrils?

Answer: Els cocodrils no poden volar, ja que no tenen ales ni cap estructura que els permeti elevar-se per l'aire. Aquestes criatures aquàtiques depenen de la seva força i agilitat per moure's i caçar preses a terra ferma o al riu. No obstant això, poden assolir velocitats notables quan es tracta de fugir o perseguir una presa. El cocodril més ràpid, el cocodril negre africà, pot arribar a assolir velocitats de fins a 40 km/h en terra ferma, tot i que la seva velocitat a l'aigua és molt menor.
```

```
Prompt: Explica pas a pas com resoldre l'equació següent: 2x + 10 = 0

Answer:
Per resoldre l'equació 2x + 10 = 0, segueix els passos següents:

1. Aïllar la variable: per fer-ho, hem de restar 10 de tots dos costats de l'equació. Així, tenim:

2x = -10

2. Dividir tots dos costats per 2: això ens permet obtenir una equació en forma d'una sola incògnita. Així, tenim:

x = -5

3. Comprovar: sempre és recomanable comprovar la solució en l'equació original. Substituint x = -5 en 2x + 10 = 0, obtenim:

2(-5) + 10 = -10 + 10 = 0

Com que l'equació es compleix, la solució x = -5 és vàlida.
```


## Training procedure

The model was trained **with the same prompt template of Llama-3 Instruct**.

The model was trained for two epochs on **4x A100 80GB GPUs using DeepSpeed ZeRO** State-3 without CPU offloading.


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-07
- distributed_type: multi-GPU
- num_devices: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1


## 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.