Update README.md

README.md

@@ -147,9 +147,43 @@ Custom byte-level BPE tokenizer trained from scratch on a sample of the pretrain
 
 ---
 
-## Inference 
+## Inference
 
-Here's a basic inference code you can run to immediately start using İvme-Conversate-v2-Base
+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.
+
+## Legacy Inference 
+
+Here's a basic inference code in case you want to work with the pickle files.
 
 ```python
 import sys
@@ -234,7 +268,7 @@ You can check our other upcoming models on our organization card!
 ## Citation
 
 ```bibtex
-@misc{ivme-conversate-v2-24m,
+@misc{ivme-conversate-v2-Base,
   author       = {IvmeLabs},
   title        = {İvme-Conversate-v2-Base},
   year         = {2026},