Update README.md

README.md

@@ -10,14 +10,72 @@ tags:
 license: apache-2.0
 language:
 - en
+datasets:
+- ericrisco/gsm8k-translated-catalan
+- ericrisco/gsm8k-translated-spanish
+- openai/gsm8k
 ---
+# Salamandra Model Card
 
-# Uploaded  model
+Salamandra is a highly multilingual model pre-trained from scratch that comes in different sizes. This model card corresponds to the **2B instructed version**, fine-tuned using **GRPO (Group Reward Policy Optimization)** and **Unsloth**.
 
-- **Developed by:** ericrisco
-- **License:** apache-2.0
-- **Finetuned from model :** BSC-LT/salamandra-2b-instruct
+To visit the model cards of other Salamandra versions, please refer to the Model Index.
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+The entire Salamandra family is released under a permissive Apache 2.0 license. Along with the open weights, all training scripts and configuration files are made publicly available in this GitHub repository.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## Model Details
+
+### Description
+Salamandra-2B is a **reasoning-focused** transformer-based language model fine-tuned with **GRPO**. It has been trained on **high-quality datasets**, including:
+
+- **GSM8K (English)**
+- **GSM8K Translated (Spanish)**
+- **GSM8K Translated (Catalan)**
+
+This dataset selection allows the model to **reason through complex problems** in multiple languages. Instead of relying on traditional supervised fine-tuning, **GRPO optimizes the model through reward-based reinforcement learning**, making it more adaptive to structured reasoning tasks.
+
+## Intended Use
+
+### Direct Use
+The model is designed as a **reasoning assistant** capable of structured problem-solving across different domains. It can be used for:
+- Logical and mathematical reasoning tasks
+- Multi-step question answering
+- Instruction following in multilingual contexts
+
+### Out-of-scope Use
+The model is not intended for malicious applications or any activity that violates legal or ethical standards.
+
+## How to Use
+
+The instruction-following models use the **ChatML template** for structured dialogue formatting:
+
+```python
+from datetime import datetime
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "ericrisco/salamandra-2b-grpo"
+
+text = "At what temperature does water boil?"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+message = [ { "role": "user", "content": text } ]
+date_string = datetime.today().strftime('%Y-%m-%d')
+
+prompt = tokenizer.apply_chat_template(
+    message,
+    tokenize=False,
+    add_generation_prompt=True,
+    date_string=date_string
+)
+
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))