SPAISS6F1/f1-1b-th-base-mlops-test

F1-1B-TH-Base MLOps Test

This repository contains an early MLOps checkpoint for F1-1B-TH-Base, a Thai decoder-only causal language model trained from scratch with a Qwen/Llama-style architecture.

Important

This is an infrastructure checkpoint, not a production-quality language model.

• Parameters: 1,244,760,064
• Architecture: Qwen/Llama-style decoder-only Transformer
• Training type: from scratch, random initialization
• Pretrained external weights: none
• Intended use: MLOps loading, checkpointing, training continuation, smoke generation
• Expected output quality: poor/unstable because the checkpoint was trained on a tiny token budget

Architecture

• RMSNorm
• RoPE positional embedding
• grouped-query causal self-attention
• SwiGLU feedforward
• 24 layers
• hidden size 2048
• 16 attention heads
• 8 KV heads
• context length 512
• vocab size 32000

Files

• f1_1b_th_base_final.pt: PyTorch checkpoint
• config.json: architecture and training metadata
• f1_model.py: minimal model definition for loading
• load_model.py: example loader
• tokenizer.model and tokenizer.vocab: optional tokenizer files if uploaded

Load Example

import torch
from f1_model import F1ForCausalLM, ModelConfig

cfg = ModelConfig.from_json("config.json")
model = F1ForCausalLM(cfg)
payload = torch.load("f1_1b_th_base_final.pt", map_location="cpu")
state = payload["model"] if isinstance(payload, dict) and "model" in payload else payload
model.load_state_dict(state)
model.eval()

Status

This checkpoint is for MLOps validation only. Train or continue-train on substantially more Thai data before using it for evaluation, demos, or downstream applications.