Update README.md

README.md

@@ -1,3 +1,128 @@
----
-license: mit
----
+---
+license: apache-2.0
+datasets:
+- kaifkhaan/roast
+base_model:
+- mistralai/Mistral-7B-Instruct-v0.1
+tags:
+- not-for-all-audiences
+---
+# Mistral Roast bot
+ 
+Welcome to the Mistral Roastbot model repository! This model has been fine-tuned on custom Roast data and is designed to generate Uncensored Roasts  in the context of any queries from the user.
+ 
+## Model Details
+ 
+- **Model Name**: Mistral Roast bot
+- **Model Architecture**: [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+- **Model Type**: Causal Language Model
+- **Training Data**: Fine-tuned on a custom dataset of kaifkhaan/roast.
+ 
+## Usage
+ 
+### Installation
+ 
+To use this model, you will need the `transformers` and `llama_cpp` libraries installed. You can install them using pip:
+ 
+```bash
+pip install transformers llama_cpp bitsandbytes accelerate
+```
+ 
+```bash
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig , pipeline
+import torch
+
+# Configuration for the nf4 quantization
+nf4_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+
+
+# Load the fine-tuned model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "kaifkhaan/Roast_Bot",  
+    device_map='auto',
+    quantization_config=nf4_config,
+    use_cache=False
+)
+
+tokenizer = AutoTokenizer.from_pretrained("kaifkhaan/Roast_Bot" )
+
+# Ensure pad token and padding side are correctly set
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+def generate_response(prompt, model, tokenizer):
+    # Add the custom instruction and format the prompt
+    #try giving different instruction as per your need and have fun !
+    instruction = "You are a chatbot that will roast the user while also using uncensored curse words and offensive statements while simultaneously answering his queries. Use the provided input and create a roast response also use as many curse words in the response as possible ."
+    full_prompt = f"<s>### Instruction:\n{instruction}\n\n### Input:\n{prompt}\n\n### Response:"
+
+    # Encode the prompt
+    encoded_input = tokenizer(full_prompt, return_tensors="pt", add_special_tokens=True)
+    model_inputs = encoded_input.to('cuda')
+    
+    # Generate text from the model
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=350,  # Adjust as needed
+        do_sample=True,
+        temperature=0.6,  # Control randomness
+        top_k=50,  # Limits sampling to top k tokens
+        top_p=0.95,  # Nucleus sampling
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode the generated text
+    decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    
+    # Extract the response part
+    response = decoded_output[0]
+    response = response.split("### Response:")[1].strip() if "### Response:" in response else response.strip()
+    
+    return response
+
+# Example prompt
+
+
+prompt = "am i pretty ?"
+
+# Generate the response
+response = generate_response(prompt, model, tokenizer)
+
+print(response)
+```
+ 
+```bash
+response = "you look like a sack of sh*t with a face."
+```
+ 
+### Training
+ 
+The model was fine-tuned on a custom dataset consisting of Roasts between user and the bot. The fine-tuning process involved training the model for 15 epochs using a batch size of 16 on a single GPU.
+ 
+## Hyperparameters
+- **Learning Rate**: 2e-4
+- **Batch Size**: 16
+- **Number of Epochs**: 15
+- **Optimizer**: AdamW
+ 
+ 
+### Limitations and Biases
+ 
+- **Domain Specific**: The model is fine-tuned specifically for fun and roast purpose.
+ 
+### Citation
+```bibtex
+@misc{mistral_Roastbot_2024,
+  author = {kaifkhaan},
+  title = {Mistral Roast Model},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/kaifkhaan/Roast_Bot}
+}
+```