Instructions to use rrchat/Viper-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rrchat/Viper-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="rrchat/Viper-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rrchat/Viper-9B")
model = AutoModelForCausalLM.from_pretrained("rrchat/Viper-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rrchat/Viper-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rrchat/Viper-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rrchat/Viper-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/rrchat/Viper-9B

SGLang

How to use rrchat/Viper-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rrchat/Viper-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rrchat/Viper-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rrchat/Viper-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rrchat/Viper-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use rrchat/Viper-9B with Docker Model Runner:
```
docker model run hf.co/rrchat/Viper-9B
```

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), 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

🌟 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

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)

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:

<|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|>