latamgpt-1b-sft/README.md

license, base_model, language, library_name, pipeline_tag, tags, datasets

license	base_model	language	library_name	pipeline_tag	tags	datasets
llama3.2	meta-llama/Llama-3.2-1B	es pt en	transformers	text-generation	sft latam llama latam-gpt	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

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 from the base model.