81 lines
3.8 KiB
Markdown
81 lines
3.8 KiB
Markdown
---
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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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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- trl
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- coder
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- 7B
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---
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# **Viper-Coder-HybridMini-v1.3**
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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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### **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-HybridMini-v1.3"
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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. **Moderate Computational Demand**: Requires GPUs/TPUs for smooth inference due to **7B parameters**, but more lightweight than larger models.
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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. |