Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Deepthink-Llama-3-8B-Preview`](https://huggingface.co/prithivMLmods/Deepthink-Llama-3-8B-Preview) 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/Deepthink-Llama-3-8B-Preview) for more details on the model.
 
+The Deepthink-Llama-3-8B-Preview is a fine-tuned version of the Llama-3.1-8B base model, further enhanced with the Rethinking R1 Dataset Logits
+ for superior text generation. This model is designed for advanced 
+reasoning, structured problem-solving, and contextually rich outputs, 
+making it an excellent choice for applications in education, programming, research, and creative writing.  
+
+
+With its optimized architecture, Deepthink-Llama-3-8B-Preview excels at:  
+
+
+Logical reasoning and step-by-step problem solving  
+Mathematical and coding tasks, leveraging specialized expert models  
+Generating long-form content (up to 8K tokens) with improved coherence  
+Understanding structured data, including tables and JSON outputs  
+Instruction following and adapting to diverse system prompts, making it ideal for chatbots and AI assistants
+
+
+
+	
+		
+	
+
+		Key Features
+	
+
+
+
+Supports long-context processing of up to 128K tokens  
+Multilingual capabilities for 29+ languages, including English, Chinese, Spanish, French, German, Arabic, and more  
+Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)
+
+
+
+	
+		
+	
+
+		Model Architecture
+	
+
+
+
+Deepthink-Llama-3-8B-Preview is built on the optimized transformer architecture of Llama-3.1-8B, integrating enhanced dataset logits from Rethinking R1 for better contextual understanding and output quality.  
+
+
+
+	
+		
+	
+
+		Use with transformers
+	
+
+
+
+To run conversational inference using transformers >= 4.43.0, use the pipeline abstraction or leverage the generate() function with the Auto classes.  
+
+
+Ensure your environment is updated with:  
+
+
+pip install --upgrade transformers
+
+
+
+
+	
+		
+	
+
+		Example Usage
+	
+
+
+
+import torch
+from transformers import pipeline
+
+model_id = "prithivMLmods/Deepthink-Llama-3-8B-Preview"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    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 = pipe(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+
+
+
+
+	
+		
+	
+
+		Intended Use
+	
+
+
+
+Deepthink-Llama-3-8B-Preview is designed for a wide 
+range of applications requiring deep reasoning, structured outputs, and 
+logical text generation. It is particularly suited for:  
+
+
+Education & Research: Generating detailed explanations, step-by-step solutions, and structured academic content.  
+Programming & Code Generation: Assisting in code writing, debugging, and algorithm explanations with improved logic structuring.  
+AI Chatbots & Assistants: Providing context-aware, instruction-following responses for conversational AI applications.  
+Creative Writing: Generating high-quality stories, articles, and structured narratives with coherence.  
+Data Analysis & Structured Output Generation: Interpreting and generating JSON, tables, and formatted outputs for structured data processing.
+
+
+
+	
+		
+	
+
+		Limitations
+	
+
+
+
+While Deepthink-Llama-3-8B-Preview is optimized for deep reasoning and structured outputs, it has some limitations:  
+
+
+Not a Real-time Knowledge Source  
+
+
+The model is trained on a fixed dataset and does not have real-time 
+internet access. It may not provide up-to-date information on rapidly 
+evolving topics.
+
+
+Potential Biases  
+
+
+As with all AI models, responses may reflect biases present in the 
+training data. Users should critically evaluate outputs, especially in 
+sensitive domains.
+
+
+Mathematical & Logical Reasoning Constraints  
+
+
+While strong in step-by-step reasoning, it may occasionally produce 
+incorrect mathematical calculations or logical inconsistencies. External
+ verification is recommended for critical applications.
+
+
+Handling of Extremely Long Contexts  
+
+
+While it supports up to 128K tokens, efficiency and coherence may degrade when processing very long documents or conversations.
+
+
+Limited Handling of Ambiguity  
+
+
+The model may struggle with highly ambiguous or context-dependent 
+queries, sometimes generating plausible but incorrect responses.
+
+
+Ethical & Compliance Considerations  
+
+
+Not intended for generating misinformation, automating legal or 
+medical decisions, or other high-risk applications without human 
+oversight.
+
+-
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)