--- 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|