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Model: manucif/latamgpt-1b-sft
Source: Original Platform

README.md

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+---
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B
+language:
+- es
+- pt
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- sft
+- latam
+- llama
+- latam-gpt
+datasets:
+- latam-gpt/wikipedia-qa-iberoamerica-clean
+- latam-gpt/tulu-3-sft-mixture-no-identity-clean
+- latam-gpt/sft-mcqa-unified-clean
+- latam-gpt/self-identity-v3-clean
+- latam-gpt/copuchatV2-clean
+---
+
+# latamgpt-1b-sft
+
+Full supervised fine-tune of `meta-llama/Llama-3.2-1B` on a Latin American instruction mixture, with sequence packing and assistant-only loss masking.
+
+## Training data
+
+| Dataset | Role |
+|---|---|
+| `latam-gpt/wikipedia-qa-iberoamerica-clean` | Iberoamerican QA |
+| `latam-gpt/tulu-3-sft-mixture-no-identity-clean` | General SFT mixture |
+| `latam-gpt/sft-mcqa-unified-clean` | Multiple-choice QA |
+| `latam-gpt/self-identity-v3-clean` | Self-identity |
+| `latam-gpt/copuchatV2-clean` | Chilean conversational |
+
+After tokenization with the Llama 3 chat template and FFD packing at 4096 tokens (99.16% packing efficiency), the training set is **175,995 packed sequences (~721M tokens)** with **119,573 validation examples**.
+
+## Training setup
+
+- **Base model:** `meta-llama/Llama-3.2-1B`
+- **Method:** Full fine-tune (no LoRA)
+- **Sequence length:** 4096, packed with assistant-only loss masking
+- **Epochs:** 2
+- **Learning rate:** 1e-5 (cosine, 3% warmup)
+- **Batch:** 8 per device × 8 grad accumulation × 2 GPUs = **128 effective**
+- **Precision:** bf16 + DeepSpeed ZeRO-2 + gradient checkpointing + Flash Attention 2
+- **Hardware:** 2× H100 80GB
+- **Wall time:** ~7.5h
+
+## Results
+
+| Metric | Value |
+|---|---|
+| `train_loss` | 0.8579 |
+| `eval_loss` | 0.8910 |
+| `epoch` | 2.0 |
+| `train_samples` | 175,995 |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tok = AutoTokenizer.from_pretrained("latam-gpt/latamgpt-1b-sft")
+model = AutoModelForCausalLM.from_pretrained("latam-gpt/latamgpt-1b-sft", torch_dtype="bfloat16", device_map="auto")
+
+messages = [{"role": "user", "content": "¿Cuál es la capital de Chile?"}]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
+print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## License
+
+Inherits the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/) from the base model.