Update README.md

README.md

@@ -122,6 +122,86 @@ model-index:
 This model was converted to GGUF format from [`prithivMLmods/QwQ-LCoT2-7B-Instruct`](https://huggingface.co/prithivMLmods/QwQ-LCoT2-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/QwQ-LCoT2-7B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+The QwQ-LCoT2-7B-Instruct is a fine-tuned language model 
+designed for advanced reasoning and instruction-following tasks. It 
+leverages the Qwen2.5-7B base model and has been fine-tuned on the chain
+ of thought reasoning datasets, focusing on chain-of-thought (CoT) 
+reasoning for problems. 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.
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"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
+
+The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning 
+and instruction-following tasks, with specific applications including:  
+
+Instruction Following: Providing detailed and step-by-step guidance for a wide range of user queries.
+Logical Reasoning: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
+Text Generation: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
+Problem-Solving: Analyzing and addressing tasks 
+that require chain-of-thought (CoT) reasoning, making it ideal for 
+education, tutoring, and technical support.
+Knowledge Enhancement: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
+
+		Limitations
+	
+Data Bias: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
+Context Limitation: Performance may degrade for 
+tasks requiring knowledge or reasoning that significantly exceeds the 
+model's pretraining or fine-tuning context.
+Complexity Ceiling: While optimized for multi-step 
+reasoning, exceedingly complex or abstract problems may result in 
+incomplete or incorrect outputs.
+Dependency on Prompt Quality: The quality and specificity of the user prompt heavily influence the model's responses.
+Non-Factual Outputs: Despite being fine-tuned for 
+reasoning, the model can still generate hallucinated or factually 
+inaccurate content, particularly for niche or unverified topics.
+Computational Requirements: Running the model 
+effectively requires significant computational resources, particularly 
+when generating long sequences or handling high-concurrency workloads.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)