| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| base_model: |
| - Qwen/Qwen2.5-7B-Instruct |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - text-generation-inference |
| - trl |
| - coder |
| - 7B |
| model-index: |
| - name: Viper-Coder-HybridMini-v1.3 |
| 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: 61.04 |
| name: averaged accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| 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: 33.67 |
| name: normalized accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| 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: 46.3 |
| name: exact match |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| 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: 8.95 |
| name: acc_norm |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| 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: 15.61 |
| name: acc_norm |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| 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: 37.24 |
| name: accuracy |
| source: |
| url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FViper-Coder-HybridMini-v1.3 |
| name: Open LLM Leaderboard |
| --- |
|  |
|
|
| # **Viper-Coder-HybridMini-v1.3** |
|
|
| 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**. |
|
|
| ### **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-HybridMini-v1.3" |
| |
| 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. **Moderate Computational Demand**: Requires GPUs/TPUs for smooth inference due to **7B parameters**, but more lightweight than larger models. |
| 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-HybridMini-v1.3-details)! |
| Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FViper-Coder-HybridMini-v1.3&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
|
|
| | Metric |Value (%)| |
| |-------------------|--------:| |
| |**Average** | 33.80| |
| |IFEval (0-Shot) | 61.04| |
| |BBH (3-Shot) | 33.67| |
| |MATH Lvl 5 (4-Shot)| 46.30| |
| |GPQA (0-shot) | 8.95| |
| |MuSR (0-shot) | 15.61| |
| |MMLU-PRO (5-shot) | 37.24| |
|
|
|
|
