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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- prithivMLmods/Viper-Coder-v1.4 |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- trl |
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- text-generation-inference |
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- coder |
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- viper |
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--- |
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# **Viper-Coder-v1.5-r999** |
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> Viper-Coder-v1.5-r999 is based on the Qwen 2.5 14B 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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### **Key Improvements** |
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1. **Best-in-Class Coding Proficiency**: Enhanced understanding of programming languages, debugging, and code generation. |
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2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (**8K+ tokens**). |
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3. **Advanced Logical & Mathematical Reasoning**: Improved multi-step problem-solving and theorem proving. |
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4. **Long-Context Mastery**: Handles up to **128K tokens** with an output capability of **8K tokens** per response. |
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5. **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** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Viper-Coder-v1.5-r999" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto", |
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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Write a Python function to merge two sorted lists." |
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messages = [ |
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{"role": "system", "content": "You are an advanced AI assistant with expert-level coding and reasoning abilities."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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### **Intended Use** |
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- **Elite Coding & Debugging**: Best-in-class model for writing, analyzing, and optimizing code. |
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- **Complex Algorithmic Reasoning**: Solves intricate logic problems and algorithm-based challenges. |
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- **Scientific & Mathematical Computation**: Advanced support for formulas, equations, and theorem verification. |
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- **Structured Data Processing**: Seamlessly handles JSON, XML, SQL, and data pipeline automation. |
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- **Multilingual Programming Support**: Proficient in Python, JavaScript, C++, Java, Go, and more. |
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- **Extended Technical Content Generation**: Ideal for writing documentation, research papers, and technical blogs. |
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### **Limitations** |
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1. **High Computational Demand**: Requires powerful GPUs/TPUs for smooth inference due to **14B parameters**. |
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2. **Language-Specific Variability**: Performance may vary across different programming languages. |
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3. **Possible Error Propagation**: Extended text outputs might introduce logical inconsistencies. |
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4. **Limited Real-World Awareness**: The model does not have access to real-time internet updates. |
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5. **Prompt Sensitivity**: Performance depends on how well the prompt is structured. |
