Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Viper-Coder-HybridMini-v1.3`](https://huggingface.co/prithivMLmods/Viper-Coder-HybridMini-v1.3) 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/Viper-Coder-HybridMini-v1.3) for more details on the model.
 
+---
+Viper-Coder-HybridMini-v1.3
+-
+
+
+Viper-Coder-HybridMini-v1.3 is based on the Qwen 2.5 7B modality architecture, designed to be the best
+ for coding and reasoning tasks. It has been fine-tuned on a synthetic 
+dataset leveraging the latest coding logits and CoT datasets, further 
+optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex coding tasks, instruction-following, and text generation.  
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+		
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+Key Improvements
+-
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+
+Best-in-Class Coding Proficiency: Enhanced understanding of programming languages, debugging, and code generation.  
+Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens).  
+Advanced Logical & Mathematical Reasoning: Improved multi-step problem-solving and theorem proving.  
+Long-Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response.  
+Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages.
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+Quickstart with Transformers
+-
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Viper-Coder-HybridMini-v1.3"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to merge two sorted lists."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level coding and reasoning abilities."},
+    {"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]
+print(response)
+
+
+
+
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+Intended Use
+-
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+
+Elite Coding & Debugging: Best-in-class model for writing, analyzing, and optimizing code.  
+Complex Algorithmic Reasoning: Solves intricate logic problems and algorithm-based challenges.  
+Scientific & Mathematical Computation: Advanced support for formulas, equations, and theorem verification.  
+Structured Data Processing: Seamlessly handles JSON, XML, SQL, and data pipeline automation.  
+Multilingual Programming Support: Proficient in Python, JavaScript, C++, Java, Go, and more.  
+Extended Technical Content Generation: Ideal for writing documentation, research papers, and technical blogs.
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+Limitations
+-
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+Moderate Computational Demand: Requires GPUs/TPUs for smooth inference due to 7B parameters, but more lightweight than larger models.  
+Language-Specific Variability: Performance may vary across different programming languages.  
+Possible Error Propagation: Extended text outputs might introduce logical inconsistencies.  
+Limited Real-World Awareness: The model does not have access to real-time internet updates.  
+Prompt Sensitivity: Performance depends on how well the prompt is structured.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)