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