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README.md

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 # **Llama-8B-Distill-CoT**
 
-Llama-8B-Distill-CoT is based on the *Llama [ KT ]* model, distilled by DeepSeek-R1-Distill-Llama-8B. 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.
+Llama-8B-Distill-CoT is based on the *Llama [ KT ]* model, distilled by DeepSeek-R1-Distill-Llama-8B. 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.
+
+
+# **Use with transformers**
+
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import transformers
+import torch
+
+model_id = "prithivMLmods/Llama-8B-Distill-CoT"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
+### **Intended Use:**  
+1. **Instruction-Following:** The model is designed to handle detailed instructions, making it ideal for virtual assistants, automation tools, and educational platforms.  
+2. **Problem-Solving:** Its fine-tuning on chain-of-thought (CoT) reasoning allows it to tackle multi-step problem-solving in domains such as mathematics, logic, and programming.  
+3. **Text Generation:** Capable of generating coherent and contextually relevant content, it is suitable for creative writing, documentation, and report generation.  
+4. **Education and Training:** Provides step-by-step explanations and logical reasoning, making it a useful tool for teaching and learning.  
+5. **Research and Analysis:** Supports researchers and professionals by generating detailed analyses and structured arguments for complex topics.  
+6. **Programming Assistance:** Helps in generating, debugging, and explaining code, as well as creating structured outputs like JSON or XML.  
+
+### **Limitations:**  
+1. **Resource Intensive:** Requires high computational resources to run efficiently, which may limit accessibility for small-scale deployments.  
+2. **Hallucination Risk:** May generate incorrect or misleading information, especially when handling ambiguous or poorly framed prompts.  
+3. **Domain-Specific Gaps:** While fine-tuned for reasoning, it may not perform well in specialized domains outside its training data.  
+4. **Bias in Training Data:** The model's responses can reflect biases present in the datasets it was trained on, potentially leading to biased or inappropriate outputs.  
+5. **Dependence on Input Quality:** Performance heavily depends on clear, structured inputs. Ambiguous or vague queries can result in suboptimal outputs.  
+6. **Limited Real-Time Context:** The model cannot access real-time information or updates beyond its training data, potentially affecting its relevance for time-sensitive queries.  
+7. **Scalability for Long-Context:** While capable of multi-step reasoning, its ability to handle extremely long or complex contexts may be limited compared to larger, more specialized models.