Update README.md

README.md

@@ -12,4 +12,68 @@ tags:
 - trl
 - coder
 - 7B
----
+---
+# **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**.  
+
+### **Key Improvements**  
+1. **Best-in-Class Coding Proficiency**: Enhanced understanding of programming languages, debugging, and code generation.  
+2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (**8K+ tokens**).  
+3. **Advanced Logical & Mathematical Reasoning**: Improved multi-step problem-solving and theorem proving.  
+4. **Long-Context Mastery**: Handles up to **128K tokens** with an output capability of **8K tokens** per response.  
+5. **Multilingual Code Support**: Excels in **Python, JavaScript, C++, Java, SQL**, and other major programming languages, with documentation in **29+ languages**.  
+
+### **Quickstart with Transformers**  
+
+```python
+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)
+```
+
+### **Intended Use**  
+- **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.  
+
+### **Limitations**  
+1. **Moderate Computational Demand**: Requires GPUs/TPUs for smooth inference due to **7B parameters**, but more lightweight than larger models.  
+2. **Language-Specific Variability**: Performance may vary across different programming languages.  
+3. **Possible Error Propagation**: Extended text outputs might introduce logical inconsistencies.  
+4. **Limited Real-World Awareness**: The model does not have access to real-time internet updates.  
+5. **Prompt Sensitivity**: Performance depends on how well the prompt is structured.