Update README.md

README.md

@@ -29,7 +29,7 @@ datasets:
 
 # KeyLM-75M-Instruct
 
-KeyLM-75M-Instruct is a 75M parameter instruction-tuned language model trained from scratch on approximately 18 billion tokens. That training budget is a small fraction of what comparable small models use (SmolLM-135M was trained on roughly 600B tokens, SmolLM2-135M on roughly 2T). Despite this, it is competitive on instruction following, outperforming SmolLM-135M-Instruct on IFEval while using about half the parameters and a fraction of the data.
+KeyLM-75M-Instruct is a 75M parameter instruction-tuned language model trained from scratch on approximately 18 billion tokens. That training budget is a small fraction of what comparable small models use (SmolLM-135M was trained on 600B tokens and SmolLM2-135M on 2T). Despite this, it is competitive on instruction following, outperforming SmolLM-135M-Instruct on IFEval while using about half the parameters and a fraction of the data.
 
 ## Table of Contents
 
@@ -60,8 +60,6 @@ GGUF builds for `llama.cpp`, LM Studio, and Ollama are available at [KeyLM-75M-I
 
 ## How to Use
 
-KeyLM ships its own modeling code, so load it with `trust_remote_code=True`. It requires `transformers>=4.51`.
-
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -83,8 +81,6 @@ outputs = model.generate(
 print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
 ```
 
-The model uses a plain `User:` / `Assistant:` chat format, applied automatically by `apply_chat_template`. Assistant turns end with `</s>`.
-
 ## Evaluation
 
 ### Instruction following (IFEval)