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--- |
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license: llama3 |
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base_model: catallama/CataLlama-v0.1-Base |
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tags: |
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- llama |
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- llama-3 |
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- Catalan |
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model-index: |
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- name: CataLlama-v0.1-Instruct-SFT |
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results: [] |
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datasets: |
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- catallama/Catalan-Instruct |
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language: |
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- ca |
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- en |
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pipeline_tag: text-generation |
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--- |
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# CataLlama-8B-v0.1-Instruct-SFT |
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**CataLlama-v0.1-Instruct-SFT** is an instruct fine-tune of [catallama/CataLlama-v0.1-Base](https://huggingface.co/catallama/CataLlama-v0.1-Base) on the [catallama/Catalan-Instruct](https://huggingface.co/datasets/catallama/Catalan-Instruct) dataset. |
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The model shows improved proficiency with the Catalan language. |
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**This is an instruction fine-tuned model proficient on the following tasks in Catalan** |
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- *Information extraction (suitable for RAG)* |
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- *Named Entity Recognition (NER)* |
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- *Translation from English to Catalan and Catalan to English* |
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- *Summarization - both short form and long form* |
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- *Chat* |
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- *Sentiment analysis* |
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- *Open question answering* |
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The model achieves a loss rate of 0.8528 on the validation dataset after two epochs. |
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**NOTE:** The model was trained for one epoch on the `train` split of dataset and after manual evaluation, I decided to go for another epoch. |
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The first epoch logs every 100 steps while the second epoch logs every 200 steps, but I am pasting the train and eval losses for both epochs bellow. |
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*The `train` split of the dataset was shuffled before the second epoch. The `test` split dataset is identical in both epochs without shuffling* |
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**Model developers** [Laurentiu Petrea](https://www.linkedin.com/in/laurentiupetrea/) based on Llama-3 from Meta. |
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**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. |
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**License** The model uses the llama-3 license available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) |
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### Use with transformers |
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See the snippet below for usage with Transformers: |
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**The model follows the same prompt template as Llama-3 Instruct** |
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```python |
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import transformers |
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import torch |
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model_id = "catallama/CataLlama-v0.1-Base" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "user", "content": "Ei com estàs avui?"}, |
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] |
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prompt = pipeline.tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=1024, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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## Training procedure |
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The model was trained **with the same prompt template of Llama-3 Instruct**. |
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The model was trained for two epochs on **6x A100 80GB GPUs using DeepSpeed ZeRO** State-3 without CPU offloading. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- distributed_type: multi-GPU |
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- num_devices: 6 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 2 |
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### Training results |
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**Epoch 1** |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.0938 | 0.11 | 100 | 1.0779 | |
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| 1.0186 | 0.22 | 200 | 1.0209 | |
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| 1.0157 | 0.32 | 300 | 0.9808 | |
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| 0.9588 | 0.43 | 400 | 0.9489 | |
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| 0.9039 | 0.54 | 500 | 0.9244 | |
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| 0.9111 | 0.65 | 600 | 0.9086 | |
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| 0.8918 | 0.75 | 700 | 0.8961 | |
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| 0.8971 | 0.86 | 800 | 0.8886 | |
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| 0.8631 | 0.97 | 900 | 0.8846 | |
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**Epoch 2** |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.8002 | 0.22 | 200 | 0.8989 | |
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| 0.8068 | 0.43 | 400 | 0.8835 | |
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| 0.7722 | 0.65 | 600 | 0.8654 | |
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| 0.7805 | 0.86 | 800 | 0.8528 | |
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## Intended Use |
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**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. |
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**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. |
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**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**. |
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**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. |
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