Update README.md

README.md

@@ -2,45 +2,105 @@
 license: llama3
 base_model: catallama/CataLlama-v0.2-Instruct-SFT
 tags:
-- trl
-- dpo
-- generated_from_trainer
+- llama
+- llama-3
+- catalan
 model-index:
 - name: CataLlama-v0.2-Instruct-DPO
   results: []
+datasets:
+- catallama/Catalan-DPO-V2
+language:
+- ca
+- en
+pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+![](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT/resolve/main/CataLlama-v0.2.png)
 
 # CataLlama-v0.2-Instruct-DPO
 
-GSM8K: 60.05
-MMLU: 58.89
+**CataLlama-v0.2-Instruct-DPO** is a DPO fine-tune of [catallama/CataLlama-v0.2-Instruct-SFT](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT) on the [catallama/Catalan-DPO-V2](https://huggingface.co/datasets/catallama/Catalan-DPO-V2) dataset.
+
+CataLlama-v0.2 was trained on roughly **620 million new tokens** which is almost 40% more than CataLlama-v0.1.
+
+The DPO-V2 dataset has been completely rebuilt and it's almost twice the size of the DPO-V1 dataeset.
+
+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*
+- *Chat*
+
+**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
 
 | Model              | CataLlama-v0.1-Instruct-DPO | CataLlama-v0.2-Instruct-DPO     |
 | ------------------ | --------------------------- | ------------------------------- |
 | MMLU 5 shot        | 47.34                       | **58.89**                       |
-| GSM8K cot 8 shot   | 43.29                       | **60.05**                       |
+| GSM8K CoT 8 shot   | 43.29                       | **60.05**                       |
 
-This model is a fine-tuned version of [catallama/CataLlama-v0.2-Instruct-SFT](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT) on an unknown dataset.
 
-## Model description
+### Use with transformers
 
-More information needed
+See the snippet below for usage with Transformers:
 
-## Intended uses & limitations
+**The model follows the same prompt template as Llama-3 Instruct**
 
-More information needed
+```python
+import transformers
+import torch
 
-## Training and evaluation data
+model_id = "catallama/CataLlama-v0.2-Instruct-DPO"
 
-More information needed
+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 3 hours for a total GPU cost of 45€.
+
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -57,13 +117,12 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 200
 - num_epochs: 2
 
-### Training results
+## 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.
 
-### Framework versions
+**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**.
 
-- Transformers 4.38.1
-- Pytorch 2.1.1+cu121
-- Datasets 2.16.1
-- Tokenizers 0.15.2
+**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.