README.md

---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/Viper-Coder-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
- coder
- v1.1
model-index:
- name: Viper-Coder-v1.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 44.32
      name: averaged accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 49.27
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 54.61
      name: exact match
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 20.13
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 26.21
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 47.02
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-v1.1
      name: Open LLM Leaderboard
---

![viper.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/d0ZS41mS_3JpmDBroLM2z.png)

# **Viper-Coder-v1.1**  

Viper-Coder-v1.1 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-v1.1"

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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Viper-Coder-v1.1-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FViper-Coder-v1.1&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    40.26|
|IFEval (0-Shot)    |    44.32|
|BBH (3-Shot)       |    49.27|
|MATH Lvl 5 (4-Shot)|    54.61|
|GPQA (0-shot)      |    20.13|
|MuSR (0-shot)      |    26.21|
|MMLU-PRO (5-shot)  |    47.02|