## Transformers / safetensors support

This model can now be loaded with `AutoModelForCausalLM` instead of the
manual pickle-loading workflow, and weights are available as
`model.safetensors`.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True, dtype=torch.float32,
)
tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True)
model.eval()

inputs = tokenizer("Once upon a time, there was a", return_tensors="pt")
out = model.generate(
    **inputs, max_new_tokens=200, do_sample=True,
    temperature=0.8, top_k=50, pad_token_id=tokenizer.pad_token_id,
)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

`trust_remote_code=True` is required (custom architecture: RoPE + SwiGLU +
RMSNorm dense decoder). The original `ckpt_final.pt` pickle checkpoint and
`model/` architecture source remain in this repo unchanged for backwards
compatibility.

**Note on batch generation:** use left-padding
(`tokenizer.padding_side = "left"`) — the model doesn't use an explicit
attention mask over padded positions, so right-padding within a batch will
give incorrect results.