Create README.md

README.md

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