| # Micro-Distilled GRPO+VAE Model |
|
|
| ## Model Description |
| This is a distilled language model trained using Group Relative Policy Optimization (GRPO) with VAE filtering. |
|
|
| ## Model Details |
| - **Model type**: micro-distill-grpo-vae |
| - **Model size**: 42M parameters |
| - **Language**: English |
| - **License**: Apache 2.0 |
|
|
| ## Training Methodology |
| - **GRPO (Group Relative Policy Optimization)**: 8 groups |
| - **VAE Filtering**: 32D latent space |
| - **KV-Cache Reuse**: 512 cache size |
|
|
| ## Architecture Details |
| - Hidden size: 512 |
| - Number of layers: 8 |
| - Attention heads: 8 |
| - Vocabulary size: 50257 |
| - Maximum sequence length: 1024 |
|
|
| ## Usage |
|
|
| ### Using Transformers |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("micro-distill-grpo-vae") |
| tokenizer = AutoTokenizer.from_pretrained("micro-distill-grpo-vae") |
| |
| inputs = tokenizer("Hello, world!", return_tensors="pt") |
| outputs = model.generate(**inputs, max_length=50) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
