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---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/Viper-Coder-v1.4
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- text-generation-inference
- coder
- viper
---
# **Viper-Coder-v1.5-r999**
> 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**.
### **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-v1.5-r999"
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. **High Computational Demand**: Requires powerful GPUs/TPUs for smooth inference due to **14B parameters**.
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. |