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README.md

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 ![coderx.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ncJZH_SSIpEr16oAq4qDF.png)
+# **Viper-Coder-v0.1**  
+
+Viper-Coder-v0.1 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a synthetic dataset based on 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 reasoning tasks, instruction-following, and text generation.  
+
+### **Key Improvements**  
+1. **Enhanced Knowledge and Expertise**: Improved mathematical reasoning, coding proficiency, and structured data processing.  
+2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).  
+3. **Greater Adaptability**: Better role-playing capabilities and resilience to diverse system prompts.  
+4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output.  
+5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.  
+
+### **Quickstart with Transformers**  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Viper-Coder-v0.1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language models."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
+    {"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**  
+- **Advanced Reasoning & Context Understanding**: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.  
+- **Mathematical & Scientific Problem-Solving**: Enhanced capabilities for calculations, theorem proving, and scientific queries.  
+- **Code Generation & Debugging**: Generates and optimizes code across multiple programming languages.  
+- **Structured Data Analysis**: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.  
+- **Multilingual Applications**: High proficiency in over 29 languages, enabling global-scale applications.  
+- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.  
+
+### **Limitations**  
+1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference.  
+2. **Language-Specific Variability**: Performance may vary across supported languages, especially for low-resource languages.  
+3. **Potential Error Accumulation**: Long-text generation can sometimes introduce inconsistencies over extended outputs.  
+4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.  
+5. **Prompt Sensitivity**: Outputs can depend on the specificity and clarity of the input prompt.