Update README.md

README.md

@@ -1,199 +1,137 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- gl
+- es
+base_model:
+- BSC-LT/salamandra-7b-instruct
 ---
 
-# Model Card for Model ID
+# Carballo-Science
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Table of Contents
+<details>
+<summary>Click to expand</summary>
 
+- [Carballo-Legal](#carballo-legal)
+  - [Table of Contents](#table-of-contents)
+  - [Model description](#model-description)
+  - [Intended uses and limitations](#intended-uses-and-limitations)
+  - [How to use](#how-to-use)
+  - [Training](#training)
+    - [Tools](#tools)
+    - [Training data](#training-data)
+    - [Training hyperparameters](#training-hyperparameters)
+    - [Framework](#framework)
+  - [Evaluation](#evaluation)
+  - [Additional information](#additional-information)
+    - [Funding](#funding)
+    - [Cite this model](#cite-this-model)
 
+</details>
 
-## Model Details
+## Model description
 
-### Model Description
+**Carballo-Science** is a specialized 7B-parameter instruction-tuned model designed for **scientific text understanding and generation** in **Galician (GL)** and **Spanish (ES)**.
 
-<!-- Provide a longer summary of what this model is. -->
+It is based on the foundation model [BSC-LT/salamandra-7b-instruct](https://huggingface.co/BSC-LT/salamandra-7b-instruct) and has been further trained on high-quality scientific corpora extracted from diverse sources.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Intended uses and limitations
 
-### Model Sources [optional]
+**Intended uses**
+- Scientific-oriented text generation (summaries, rephrasing, explanations).    
+- Chat-style scientific assistance (non-professional).  
 
-<!-- Provide the basic links for the model. -->
+**Limitations**
+- May produce incomplete or incorrect scientific statements.  
+- Not suitable for high-stakes or science decision-making.  
+- Works best for GL and ES; other languages are not reinforced in this checkpoint.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## How to use
 
-## Uses
+```python
+from datetime import datetime
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+model_id = "proxectonos/Carballo-Science"
 
-### Direct Use
+text = "Qué sabes sobre o Proxecto Nós?"
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+  )
 
-[More Information Needed]
+message = [ { "role": "user", "content": text } ]
+date_string = datetime.today().strftime('%Y-%m-%d')
 
-### Downstream Use [optional]
+prompt = tokenizer.apply_chat_template(
+    message,
+    tokenize=False,
+    add_generation_prompt=True,
+    date_string=date_string
+)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
+generated_tokens = outputs[0][len(inputs[0]):]
+response = self.tokenizer.decode(generated_tokens, skip_special_tokens=False).strip()
+response = response.split("<|reserved_token_1|>")[0].strip()
+print(response)
+```
 
-[More Information Needed]
+## Training
 
-### Out-of-Scope Use
+### Training data
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model was trained on a mixture of general instructions and domain-specific legal texts.
 
-[More Information Needed]
+| **Dataset Type** | **Languages** | **Sources** |
+|------------------|---------------|-------------|
+| Instruction set  | GL, ES , PT , CAT , EN | [Galician Instruction Datasets](https://github.com/proxectonos/instruction_datasets) |
+| Scientific corpus     | GL, ES        | Wikipedia, PhD Thesis |
 
-## Bias, Risks, and Limitations
+### Training hyperparameters
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **epochs:** 0.5  
+- **dtype:** bf16  
+- **block size:** 2048  
+- **total batch size:** 128  
+- **learning rate:** 2e-6  
+- **scheduler:** Linear  
+- **optimizations:**  
+  - gradient checkpointing: True  
+  - flash attention: True  
+  - liger kernels: True  
+  - DeepSpeed stage: 2  
+
+### Framework
+Training was performed at the **Galician Supercomputing Center (CESGA)** on **2 nodes** with **2× NVIDIA A100 40GB** each, totaling **4 GPUs**, across **2 days**.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+Formal evaluation is in progress.  Early observations show improved handling of legal terminology, structured documents, and administrative phrasing in GL and ES.
 
-## Model Card Authors [optional]
+## Additional information
 
-[More Information Needed]
+## Funding
+This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA
 
-## Model Card Contact
+### Cite this model
+Please cite the model as follows:
 
-[More Information Needed]
+```
+@misc{carballo_legal_2025,
+    title     = {Carballo-Science: A Science Domain Instruction-Tuned Model for Galician and Spanish},
+    author    = {Proxecto Nós Team},
+    year      = {2025},
+    publisher = {HuggingFace},
+    howpublished = {\url{https://huggingface.co/proxectonos/Carballo-Science}},
+}
+```