Update README.md

README.md

@@ -82,16 +82,14 @@ pip install torch transformers datasets
 
 ```python
 from transformers import AutoTokenizer
-from modeling_swarm import SwarmForCausalLM
-from configuration_swarm import SwarmConfig
+from transformers import  AutoModelForCausalLM, AutoConfig
 
 # Load model and tokenizer
-model = SwarmForCausalLM.from_pretrained("your-username/SAGI")
-tokenizer = AutoTokenizer.from_pretrained("your-username/SAGI")
+model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SAGI")
+tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/SAGI")
 
 # Generate text
 model.eval()
-model.reset_swarm_state()  # Reset for clean generation
 
 prompt = "Once upon a time"
 inputs = tokenizer(prompt, return_tensors="pt")
@@ -109,51 +107,6 @@ outputs = model.generate(
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-### Inspecting Swarm State
-
-```python
-# Get current swarm state
-state = model.get_swarm_state()
-print(f"Active agents: {sum(state['active'][0])}")
-print(f"Goal stack depth: {state['goal_stack_size']}")
-
-# Reset swarm state between generations
-model.reset_swarm_state()
-```
-
-### Training
-
-```python
-from datasets import load_dataset
-from transformers import Trainer, TrainingArguments
-
-# Load dataset
-dataset = load_dataset("roneneldan/TinyStories", split="train[:5%]")
-
-# Tokenize
-def tokenize(examples):
-    return tokenizer(examples["text"], truncation=True, max_length=256)
-
-tokenized = dataset.map(tokenize, batched=True, remove_columns=["text"])
-
-# Train
-training_args = TrainingArguments(
-    output_dir="./sagi-finetuned",
-    per_device_train_batch_size=4,
-    learning_rate=3e-4,
-    num_train_epochs=1,
-    save_steps=500,
-)
-
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized,
-)
-
-trainer.train()
-```
-
 ## Model Architecture Details
 
 ### Swarm Configuration
@@ -212,38 +165,10 @@ Not intended for:
 ## Citation
 
 ```bibtex
-@software{sagi2024,
+@software{sagi2026,
   title={SAGI: Swarm AGI Language Model},
-  author={},
-  year={2024},
-  url={https://huggingface.co/your-username/SAGI}
+  author={Reaperdoesntknow},
+  year={2026},
+  url={https://huggingface.co/your-reaperdoesntknow/SAGI}
 }
 ```
-
-## License
-
-Apache 2.0
-
-## Files
-
-- `config.json` - Model configuration
-- `model.safetensors` / `pytorch_model.bin` - Model weights
-- `tokenizer.json` - Tokenizer configuration
-- `modeling_swarm.py` - Model implementation (required for loading)
-- `configuration_swarm.py` - Config class (required for loading)
-- `model.py` - Swarm-7 V2.2 core system
-
-## Custom Code Notice
-
-This model requires custom code to run. When loading, use `trust_remote_code=True`:
-
-```python
-from transformers import AutoModelForCausalLM
-
-model = AutoModelForCausalLM.from_pretrained(
-    "your-username/SAGI",
-    trust_remote_code=True
-)
-```
-
-Or import the classes directly from the model files as shown in the Quick Start section.