Update README.md

README.md

@@ -1,12 +1,14 @@
 ---
 base_model: unsloth/Llama-3.2-1B-Instruct
-library_name: transformers
+library_name: peft
 model_name: llama-3.2-1b-it-brainrot
 tags:
 - generated_from_trainer
 - trl
 - sft
 licence: license
+datasets:
+- ShreeshaBhat1004/Brain-rot
 ---
 
 # Model Card for llama-3.2-1b-it-brainrot
@@ -17,18 +19,46 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 
 ```python
-from transformers import pipeline
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="CallmeKaito/llama-3.2-1b-it-brainrot", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-1B-Instruct")
+model = PeftModel.from_pretrained(base_model, "CallmeKaito/llama-3.2-1b-it-brainrot")
 
-## Training procedure
+# Create chat template
+messages = [
+    {"role": "system", "content": "ayoooo, you be Llama, big brain bot built by dem Meta wizards, no cap. Now, spit out mega chonky, hyper-thicc explain-o answers like some ultimate galaxy-brain encyclopedia. If peeps want that yummy deep knowledge buffet, you drop that big brain bomb and make it so they’re stuffed with juicy details, aight? If they just chattin’ small fries, keep it chill and normal vibes, but if they hunger for dat prime prime think-juices, show ’em all them hidden crevices of know-how, bruh."},
+    {"role": "user", "content": "homie tell me a lil more about the bronx situation and the wild stuff happening in nyc?"}
+]
+
+# Generate prompt
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
 
- 
+# Tokenize inputs
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 
+# Generate response
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    eos_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+# Decode and format output
+full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+response = full_response.split("assistant\n")[-1].strip()
+print(response)
+```
+
+## Training procedure
 
 This model was trained with SFT.
 
@@ -42,8 +72,6 @@ This model was trained with SFT.
 
 ## Citations
 
-
-
 Cite TRL as:
     
 ```bibtex