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README.md

@@ -10,6 +10,7 @@ tags:
 - problem-solving
 - unsloth
 - lora
+library_name: transformers
 license: apache-2.0
 datasets:
 - openai/gsm8k
@@ -61,9 +62,57 @@ You are an advanced reasoning assistant that excels at solving complex problems.
 5. Verify your conclusions with examples or counterexamples
 ```
 
-## Usage with Unsloth
+## Usage with Transformers
 
-For optimal performance, load with Unsloth:
+The model can be loaded using standard Transformers library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "vexoolabs/Vexoo-TrailBlazer-1B"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# System prompt for reasoning
+system_prompt = "You are an advanced reasoning assistant that excels at solving complex problems."
+user_question = "If a train travels at 60 miles per hour, how far will it travel in 2.5 hours?"
+
+# Format with system prompt
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_question}
+]
+
+# Format prompt
+inputs = tokenizer.apply_chat_template(
+    messages,
+    return_tensors="pt",
+    add_generation_prompt=True
+).to(model.device)
+
+# Generate
+outputs = model.generate(
+    inputs,
+    max_new_tokens=300,
+    temperature=0.2,
+    top_p=0.92,
+    repetition_penalty=1.05,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Advanced Usage with Unsloth
+
+For optimal performance, you can also load with Unsloth:
 
 ```python
 # Import unsloth first
@@ -88,38 +137,3 @@ tokenizer = get_chat_template(tokenizer, chat_template="llama-3.1")
 # Prepare for inference
 FastLanguageModel.for_inference(model)
 ```
-
-## Example Usage
-
-```python
-# Create messages with system prompt
-messages = [
-    {"role": "system", "content": "You are an advanced reasoning assistant that excels at solving complex problems."},
-    {"role": "user", "content": "If a train travels at 60 miles per hour, how far will it travel in 2.5 hours?"}
-]
-
-# Apply chat template
-inputs = tokenizer.apply_chat_template(
-    messages,
-    tokenize=True,
-    add_generation_prompt=True,
-    return_tensors="pt"
-).to(model.device)
-
-# Generate response
-with torch.no_grad():
-    outputs = model.generate(
-        inputs,
-        max_new_tokens=300,
-        temperature=0.2,
-        top_p=0.92,
-        repetition_penalty=1.05,
-        do_sample=True,
-        pad_token_id=tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-
-# Decode response
-response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
-print(response)
-```