Unsloth Model Card

README.md

@@ -1,74 +1,21 @@
 ---
-library_name: transformers
+base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- gemma3_text
 license: apache-2.0
+language:
+- en
 ---
 
-## INFERENCE
+# Uploaded finetuned  model
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
-import torch
+- **Developed by:** Mr-Vicky-01
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
 
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/gemma-qna")
-model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/gemma-qna")
+This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-# Define the system prompt
-prompt = """
-<bos><start_of_turn>user
-You are Securitron, Created by Aquilax, a helpful AI assistant specialized in providing accurate and professional responses. Always prioritize clarity and precision in your answers.
-"""
-
-# Initialize conversation history
-conversation_history = []
-
-# Set up device
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-i=0
-while True:
-    user_prompt = input("\nUser Question: ")
-    if user_prompt.lower() == 'break':
-        break
-
-    if i==0:
-         user_message = f"""
-{user_prompt}<end_of_turn>
-<start_of_turn>model"""
-         i+=1
-    else:
-        user_message = f"""
-<start_of_turn>user
-{user_prompt}<end_of_turn>
-<start_of_turn>model"""
-
-    # Add the user's question to the conversation history
-    conversation_history.append(user_message)
-
-    # Keep only the last 2 exchanges (4 turns)
-    conversation_history = conversation_history[-5:]
-
-    # Build the full prompt
-    current_prompt = prompt + "\n".join(conversation_history)
-
-    # Tokenize the prompt
-    encodeds = tokenizer(current_prompt, return_tensors="pt", truncation=True).input_ids.to(device)
-
-    # Initialize TextStreamer for real-time token generation
-    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
-
-    # Generate response with TextStreamer
-    response = model.generate(
-        input_ids=encodeds,
-        streamer=text_streamer,
-        max_new_tokens=2048,
-        use_cache=True,
-        pad_token_id=106,
-        eos_token_id=106,
-        num_return_sequences=1
-    )
-
-    # Finalize conversation history with the assistant's response
-    conversation_history.append(tokenizer.decode(response[0]).split('<start_of_turn>model')[-1].split('<end_of_turn>')[0].strip())
-```
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)