--- base_model: - jdopensource/JoyAI-LLM-Flash --- ## 1. Model Introduction JoyAI-LLM Flash is a state-of-the-art medium-sized instruct language model with 3 billion activated parameters and 48 billion total parameters. JoyAI-LLM Flash was pretrained on 20 trillion text tokens using Muon optimizer, followed by large-scale supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) across diverse environments. JoyAI-LLM Flash achieves strong performance across frontier knowledge, reasoning, coding tasks and agentic capabilities. ### Key Features - Fiber Bundle RL: Introduces fiber bundle theory into reinforcement learning, proposing a novel optimization framework, FiberPO. This method is specifically designed to handle the challenges of large-scale and heterogeneous agent training, improving stability and robustness under complex data distributions. - Training-Inference Collaboration: apply Muon optimizer with dense MTP, develop novel optimization techniques to resolve instabilities while scaling up, delivering 1.3× to 1.7× the throughput of the non-MTP version. - Agentic Intelligence: designed for tool use, reasoning, and autonomous problem-solving. ## 2. Model Summary | | | | :-----------------------------------------: | :----------------------: | | **Architecture** | Mixture-of-Experts (MoE) | | **Total Parameters** | 48B | | **Activated Parameters** | 3B | | **Number of Layers** (Dense layer included) | 40 | | **Number of Dense Layers** | 1 | | **Attention Hidden Dimension** | 2048 | | **MoE Hidden Dimension** (per Expert) | 768 | | **Number of Attention Heads** | 32 | | **Number of Experts** | 256 | | **Selected Experts per Token** | 8 | | **Number of Shared Experts** | 1 | | **Vocabulary Size** | 129K | | **Context Length** | 128K | | **Attention Mechanism** | MLA | | **Activation Function** | SwiGLU | | | | ## 3. Evaluation Results

Benchmark	^{JoyAI-LLM Flash}	^{Qwen3-30B-A3B-Instuct-2507}	^{GLM-4.7-Flash (Non-thinking)}
Knowledge & Alignment
MMLU	89.50	86.87	80.53
MMLU-Pro	81.02	73.88	63.62
CMMLU	87.03	85.88	75.85
GPQA-Diamond	74.43	68.69	39.90
SuperGPQA	55.00	52.00	32.00
LiveBench	72.90	59.70	43.10
IFEval	86.69	83.18	82.44
AlignBench	8.24	8.07	6.85
HellaSwag	91.79	89.90	60.84
Coding
HumanEval	96.34	95.12	74.39
LiveCodeBench	65.60	39.71	27.43
SciCode	3.08/22.92	3.08/22.92	3.08/15.11
Mathematics
GSM8K	95.83	79.83	81.88
AIME2025	65.83	62.08	24.17
MATH 500	97.10	89.80	90.90
Agentic
SWE-bench Verified	60.60	24.44	51.60
Tau2-Retail	67.55	53.51	62.28
Tau2-Airline	54.00	32.00	52.00
Tau2-Telecom	79.83	4.39	88.60
Long Context
RULER	95.60	89.66	56.12

## 4. Deployment > [!Note] > You can access JoyAI-LLM Flash API on https://docs.jdcloud.com/cn/jdaip/chat and we provide OpenAI/Anthropic-compatible API for you. > Currently, JoyAI-LLM Flash is recommended to run on the following inference engines: * vLLM * SGLang The minimum version requirement for `transformers` is `4.57.1`. Deployment examples can be found in the [Model Deployment Guide](docs/deploy_guidance.md). ## 5. Model Usage The usage demos below demonstrate how to call our official API. For third-party APIs deployed with vLLM or SGLang, please note that: > [!Note] Recommended sampling parameters: `temperature=0.6`, `top_p=1.0` ### Chat Completion This is a simple chat completion script which shows how to call JoyAI-Flash API. ```python from openai import OpenAI client = OpenAI(base_url="http://IP:PORT/v1", api_key="EMPTY") def simple_chat(client: OpenAI): messages = [ { "role": "user", "content": [ { "type": "text", "text": "which one is bigger, 9.11 or 9.9? think carefully.", } ], }, ] model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096 ) print(f"response: {response.choices[0].message.content}") if __name__ == "__main__": simple_chat(client) ``` ### Tool call Completion This is a simple toll call completion script which shows how to call JoyAI-Flash API. ```python import json from openai import OpenAI client = OpenAI(base_url="http://IP:PORT/v1", api_key="EMPTY") def my_calculator(expression: str) -> str: return str(eval(expression)) def rewrite(expression: str) -> str: return str(expression) def simple_tool_call(client: OpenAI): messages = [ { "role": "user", "content": [ { "type": "text", "text": "use my functions to compute the results for the equations: 6+1", }, ], }, ] tools = [ { "type": "function", "function": { "name": "my_calculator", "description": "A calculator that can evaluate a mathematical equation and compute its results.", "parameters": { "type": "object", "properties": { "expression": { "type": "string", "description": "The mathematical expression to evaluate.", }, }, "required": ["expression"], }, }, }, { "type": "function", "function": { "name": "rewrite", "description": "Rewrite a given text for improved clarity", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The input text to rewrite", } }, }, }, }, ] model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=messages, temperature=1.0, max_tokens=1024, tools=tools, tool_choice="auto", ) tool_calls = response.choices[0].message.tool_calls results = [] for tool_call in tool_calls: function_name = tool_call.function.name function_args = tool_call.function.arguments if function_name == "my_calculator": result = my_calculator(**json.loads(function_args)) results.append(result) messages.append({"role": "assistant", "tool_calls": tool_calls}) for tool_call, result in zip(tool_calls, results): messages.append( { "role": "tool", "tool_call_id": tool_call.id, "name": tool_call.function.name, "content": result, } ) response = client.chat.completions.create( model=model_name, messages=messages, temperature=1.0, max_tokens=1024, ) print(response.choices[0].message.content) if __name__ == "__main__": simple_tool_call(client) ``` --- ## 6. License Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).