Update README.md

README.md

@@ -11,4 +11,61 @@ tags:
 - R1
 - Qwen
 - Deepseek
----
+---
+# **Elita-0.1-Distilled-R1-Abliterated**  
+
+Elita-0.1-Distilled-R1-Abliterated is based on the *Qwen [ KT ] model*, which was distilled by *DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B*. It has been fine-tuned on the long chain-of-thought reasoning model and specialized datasets, focusing on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.  
+
+# **Quickstart with Transformers**  
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Elita-0.1-Distilled-R1-Abliterated"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Elita, created by DeepSeek-AI. You are a powerful reasoning assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+### **Intended Use:**  
+1. **Instruction-Following:** The model excels in understanding and executing detailed instructions, making it ideal for automation systems, virtual assistants, and educational tools.  
+2. **Text Generation:** It can produce coherent, logically structured, and contextually relevant text for use in content creation, summarization, and report writing.  
+3. **Complex Reasoning Tasks:** With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks.  
+4. **Research and Development:** It can support researchers and developers in exploring advancements in logical reasoning and fine-tuning methodologies.  
+5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving by generating step-by-step solutions.  
+
+### **Limitations:**  
+1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains.  
+2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.  
+3. **Bias in Training Data:** The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts.  
+4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.  
+5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.  
+6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.