| --- |
| base_model: Qwen/Qwen3.5-9B |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| tags: |
| - qwen3.5 |
| - qwen |
| - rrchat |
| - viper |
| - reasoning |
| - math |
| - code |
| - agent |
| - tool-use |
| - function-calling |
| - russian |
| - unsloth |
| license: other |
| language: |
| - ru |
| - en |
| datasets: |
| - open-r1/OpenR1-Math-220k |
| - nvidia/Nemotron-SFT-Agentic-v2 |
| - Vikhrmodels/GrandMaster-PRO-MAX |
| - nvidia/OpenMathInstruct-2 |
| - nvidia/OpenCodeInstruct |
| - m-a-p/CodeFeedback-Filtered-Instruction |
| --- |
| |
| <div align="center"> |
| <img src="viperbanner.png" alt="Viper-9B Banner" width="100%" style="border-radius: 10px; margin-bottom: 20px;"> |
| </div> |
|
|
| # Viper-9B |
|
|
| **rrchat/Viper-9B** is the flagship agentic, coding, and reasoning language model of the **RR-Gen1** lineup developed by **rrchat** ([huggingface.co/rrchat](https://huggingface.co/rrchat)), fine-tuned on top of **Qwen3.5-9B**. |
|
|
| Viper-9B is engineered for autonomous tool orchestration, structured function calling, advanced code synthesis, competitive programming, and robust bilingual Russian–English instruction following. |
|
|
| Looking for quantized GGUF weights for llama.cpp / LM Studio / Ollama? |
| 👉 **[`rrchat/Viper-9B-GGUF`](https://huggingface.co/rrchat/Viper-9B-GGUF)** |
|
|
| --- |
|
|
| ## 🌟 Key Highlights & Capabilities |
|
|
| * **Flagship RR-Gen1 Model:** The leading 9B parameter model of the RR-Gen1 family, optimized for high-complexity agentic workflows, competitive programming, and bilingual problem solving. |
| * **Autonomous Tool Use & Function Calling:** Native support for structured XML function execution (`<tool_call>...<tool_response>`) preserving near-perfect formatting and AST execution accuracy (57.0% BFCL AST match, 99.0% valid call rate). |
| * **Enhanced Competitive Coding:** Exceptional leap in algorithmic problem solving on **LiveCodeBench v6** (**33.0%** vs Base 19.0%, with **+18.2%** on Easy and **+14.7%** on Medium problems). |
| * **Python Function Synthesis:** Achieves **84.7%** on **MBPP+** (+1.4% gain over base model). |
| * **Bilingual Russian–English Math:** Delivers **44.4%** on Russian mathematical reasoning (**MGSM ru**), outperforming the base foundation model (+2.4%). |
| * **General Knowledge Retention:** Retains broad academic, STEM, and humanities knowledge with **74.0%** accuracy on **MMLU-Redux**. |
| * **Calibrated Honesty & R-Tuning:** Trained with R-Tuning to reduce confident hallucinations on unknown topics while maintaining high factuality. |
|
|
| --- |
|
|
| ## 🧪 Benchmark Highlights (vs. Base Model) |
|
|
| All benchmarks were evaluated locally on an identical test harness under strictly identical conditions: |
|
|
| | Benchmark | Mode / Harness | Base (`Qwen3.5-9B`) | Viper-9B | Delta vs. Base | Domain / Capability | |
| | :--- | :---: | :---: | :---: | :---: | :--- | |
| | **LiveCodeBench v6** | **Deterministic Pass@1** | 19.0% | **33.0%** | **+14.0%** | Competitive programming & algorithmic tasks | |
| | ↳ *LCB v6 Easy* | Deterministic Pass@1 | 42.4% | **60.6%** | **+18.2%** | Standard algorithmic challenges | |
| | ↳ *LCB v6 Medium* | Deterministic Pass@1 | 14.7% | **29.4%** | **+14.7%** | Complex dynamic programming & data structures | |
| | ↳ *LCB v6 Hard* | Deterministic Pass@1 | 0.0% | **9.1%** | **+9.1%** | Advanced competitive problem solving | |
| | **MBPP+ (seed 42)** | **EvalPlus Pass@1** | 83.3% | **84.7%** | **+1.4%** | Python function synthesis & unit tests | |
| | **MGSM ru (Math)** | **Numeric Match** | 42.0% | **44.4%** | **+2.4%** | Russian multi-step mathematical reasoning | |
| | **MMLU-Redux** | **Choice Accuracy** | 73.7% | **74.0%** | **+0.3%** | Broad multidisciplinary STEM & humanities | |
| | **BFCL v4 (Tool Use)** | **AST Function Match** | 58.0% | **57.0%** | **Parity (~57%)** | Berkeley Function Calling / Agentic Tool Calls | |
| | **IFEval (Instruction)** | **Strict Compliance** | 56.6% | **54.7%** | **Parity (~55%)** | Complex constraint & prompt compliance | |
|
|
| > [!NOTE] |
| > **Disclaimer on Evaluations:** |
| > All benchmarks presented above were conducted **manually and independently** on local test suites under controlled, strictly identical conditions (greedy non-thinking mode, max_tokens 1024, llama-server with jinja chat formatting). The results reflect real-world local execution performance and may differ from theoretical, thinking-mode, or officially reported figures published on the official base model card. |
| |
| --- |
| |
| ## 💻 Quickstart (Transformers / Python) |
| |
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_id = "rrchat/Viper-9B" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto" |
| ) |
| |
| messages = [ |
| {"role": "user", "content": "Привет! Расскажи о себе и помоги решить задачу."} |
| ] |
| |
| prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| |
| with torch.no_grad(): |
| outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.8) |
| |
| print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) |
| ``` |
| |
| --- |
| |
| ## 🛠️ Tool Calling Format |
| |
| Viper-9B utilizes structured ChatML syntax for function calling: |
| |
| ```xml |
| <|im_start|>system |
| # Tools |
| |
| You have access to the following functions: |
| |
| <tools> |
| {"name": "get_weather", "description": "Get current weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}} |
| </tools> |
| <|im_end|> |
| <|im_start|>user |
| Какая погода в Москве?<|im_end|> |
| <|im_start|>assistant |
| <tool_call> |
| <function=get_weather> |
| <parameter=location> |
| Москва |
| </parameter> |
| </function> |
| </tool_call><|im_end|> |
| ``` |
