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README.md

@@ -1,378 +1,49 @@
 ---
-language:
-- zh
-- en
-pipeline_tag: text-generation
+base_model: []
 library_name: transformers
----
-<div align="center">
-  <picture>
-      <img src="figures/joyai-logo.png" width="30%" alt="JoyAI-LLM Flash">
-  </picture>
-</div>
-<hr>
-
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/jdopensource" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-JD-ffc107?color=ffc107&logoColor=white"/></a>
-  <a href="https://huggingface.co/jdopensource/JoyAI-LLM-Flash/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
-</div>
-
-
-
-
-## 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          |
-|                   </div>                    |                          |
-
-
-## 3. Evaluation Results
-
-<table>
-<thead>
-<tr>
-<th align="center">Benchmark</th>
-<th align="center"><sup>JoyAI-LLM Flash</sup></th>
-<th align="center"><sup>Qwen3-30B-A3B-Instuct-2507</sup></th>
-<th align="center"><sup>GLM-4.7-Flash<br>(Non-thinking)</sup></th>
-</tr>
-</thead>
-<tbody>
-
-
-<tr>
-<td align="center" colspan=8><strong>Knowledge &amp; Alignment</strong></td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">MMLU</td>
-<td align="center" style="vertical-align: middle"><strong>89.50</strong></td>
-<td align="center" style="vertical-align: middle">86.87</td>
-<td align="center" style="vertical-align: middle">80.53</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">MMLU-Pro</td>
-<td align="center" style="vertical-align: middle"><strong>81.02</strong></td>
-<td align="center" style="vertical-align: middle">73.88</td>
-<td align="center" style="vertical-align: middle">63.62</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">CMMLU</td>
-<td align="center" style="vertical-align: middle"><strong>87.03</strong></td>
-<td align="center" style="vertical-align: middle">85.88</td>
-<td align="center" style="vertical-align: middle">75.85</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">GPQA-Diamond</td>
-<td align="center" style="vertical-align: middle"><strong>74.43</strong></td>
-<td align="center" style="vertical-align: middle">68.69</td>
-<td align="center" style="vertical-align: middle">39.90</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">SuperGPQA</td>
-<td align="center" style="vertical-align: middle"><strong>55.00</strong></td>
-<td align="center" style="vertical-align: middle">52.00</td>
-<td align="center" style="vertical-align: middle">32.00</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">LiveBench</td>
-<td align="center" style="vertical-align: middle"><strong>72.90</strong></td>
-<td align="center" style="vertical-align: middle">59.70</td>
-<td align="center" style="vertical-align: middle">43.10</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">IFEval</td>
-<td align="center" style="vertical-align: middle"><strong>86.69</strong></td>
-<td align="center" style="vertical-align: middle">83.18</td>
-<td align="center" style="vertical-align: middle">82.44</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">AlignBench</td>
-<td align="center" style="vertical-align: middle"><strong>8.24</strong></td>
-<td align="center" style="vertical-align: middle">8.07</td>
-<td align="center" style="vertical-align: middle">6.85</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">HellaSwag</td>
-<td align="center" style="vertical-align: middle"><strong>91.79</strong></td>
-<td align="center" style="vertical-align: middle">89.90</td>
-<td align="center" style="vertical-align: middle">60.84</td>
-</tr>
-
-<tr>
-<td align="center" colspan=8><strong>Coding</strong></td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">HumanEval</td>
-<td align="center" style="vertical-align: middle"><strong>96.34</strong></td>
-<td align="center" style="vertical-align: middle">95.12</td>
-<td align="center" style="vertical-align: middle">74.39</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">LiveCodeBench</td>
-<td align="center" style="vertical-align: middle"><strong>65.60</strong></td>
-<td align="center" style="vertical-align: middle">39.71</td>
-<td align="center" style="vertical-align: middle">27.43</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">SciCode</td>
-<td align="center" style="vertical-align: middle"><strong>3.08/22.92</strong></td>
-<td align="center" style="vertical-align: middle"><strong>3.08/22.92</strong></td>
-<td align="center" style="vertical-align: middle">3.08/15.11</td>
-</tr>
-<tr>
-<td align="center" colspan=8><strong>Mathematics</strong></td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">GSM8K</td>
-<td align="center" style="vertical-align: middle"><strong>95.83</strong></td>
-<td align="center" style="vertical-align: middle">79.83</td>
-<td align="center" style="vertical-align: middle">81.88</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">AIME2025</td>
-<td align="center" style="vertical-align: middle"><strong>65.83</strong></td>
-<td align="center" style="vertical-align: middle">62.08</td>
-<td align="center" style="vertical-align: middle">24.17</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">MATH 500</td>
-<td align="center" style="vertical-align: middle"><strong>97.10</strong></td>
-<td align="center" style="vertical-align: middle">89.80</td>
-<td align="center" style="vertical-align: middle">90.90</td>
-</tr>
-
-<tr>
-<td align="center" colspan=8><strong>Agentic</strong></td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">SWE-bench Verified</td>
-<td align="center" style="vertical-align: middle"><strong>60.60</strong></td>
-<td align="center" style="vertical-align: middle">24.44</td>
-<td align="center" style="vertical-align: middle">51.60</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">Tau2-Retail</td>
-<td align="center" style="vertical-align: middle"><strong>67.55</strong></td>
-<td align="center" style="vertical-align: middle">53.51</td>
-<td align="center" style="vertical-align: middle">62.28</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">Tau2-Airline</td>
-<td align="center" style="vertical-align: middle"><strong>54.00</strong></td>
-<td align="center" style="vertical-align: middle">32.00</td>
-<td align="center" style="vertical-align: middle">52.00</td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">Tau2-Telecom</td>
-<td align="center" style="vertical-align: middle">79.83</td>
-<td align="center" style="vertical-align: middle">4.39</td>
-<td align="center" style="vertical-align: middle"><strong>88.60</strong></td>
-</tr>
-
-<tr>
-<td align="center" colspan=8><strong>Long Context</strong></td>
-</tr>
-<tr>
-<td align="center" style="vertical-align: middle">RULER</td>
-<td align="center" style="vertical-align: middle"><strong>95.60</strong></td>
-<td align="center" style="vertical-align: middle">89.66</td>
-<td align="center" style="vertical-align: middle">56.12</td>
-</tr>
-</tbody>
-</table>
-
-
-## 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-Block-INT8 is recommended to run on the following inference engines:
-
-* SGLang
-
-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)
-
-```
+tags:
+- mergekit
+- merge
 
 ---
-
-## 6. License
-
-Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).
+# c362_step50_ta05
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [Linear DARE](https://arxiv.org/abs/2311.03099) merge method using /root/myCodeLab/host/downloads/models/40Bra as a base.
+
+### Models Merged
+
+The following models were included in the merge:
+* /root/myCodeLab/host/verl/ckpts/40bra_k8s_single_domain/40bra_k8s_16node_sd_c362_20260327_205644_unknown/global_step_50/actor/huggingface
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+base_model: /root/myCodeLab/host/downloads/models/40Bra
+dtype: float32
+merge_method: dare_linear
+modules:
+  default:
+    slices:
+    - sources:
+      - layer_range: [0, 40]
+        model: /root/myCodeLab/host/downloads/models/40Bra
+      - layer_range: [0, 40]
+        model: /root/myCodeLab/host/verl/ckpts/40bra_k8s_single_domain/40bra_k8s_16node_sd_c362_20260327_205644_unknown/global_step_50/actor/huggingface
+        parameters:
+          density: 1.0
+          weight:
+          - filter: .mlp.gate.
+            value: 0.0
+          - value: 0.5
+    - sources:
+      - layer_range: [40, 41]
+        model: /root/myCodeLab/host/downloads/models/40Bra
+out_dtype: bfloat16
+```

config.json

@@ -10,16 +10,18 @@
         "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
     },
     "bos_token_id": 0,
+    "dtype": "bfloat16",
     "eos_token_id": 1,
     "ep_size": 1,
     "first_k_dense_replace": 1,
+    "head_dim": 64,
     "hidden_act": "silu",
     "hidden_size": 2048,
     "initializer_range": 0.02,
     "intermediate_size": 7168,
     "kv_lora_rank": 512,
     "max_position_embeddings": 131072,
-    "model_type": "joyai_llm_flash",
+    "model_type":"joyai_llm_flash",
     "moe_intermediate_size": 768,
     "moe_layer_freq": 1,
     "n_group": 1,
@@ -31,7 +33,9 @@
     "num_hidden_layers": 40,
     "num_key_value_heads": 32,
     "num_nextn_predict_layers": 1,
+    "pretraining_tp": 1,
     "q_lora_rank": 1536,
+    "qk_head_dim": 192,
     "qk_nope_head_dim": 128,
     "qk_rope_head_dim": 64,
     "quantization_config": {
@@ -43,15 +47,16 @@
         ]
     },
     "rms_norm_eps": 1e-06,
+    "rope_interleave": true,
+    "rope_scaling": null,
     "rope_theta": 32000000,
     "routed_scaling_factor": 2.5,
     "scoring_func": "sigmoid",
     "tie_word_embeddings": false,
     "topk_group": 1,
     "topk_method": "noaux_tc",
-    "torch_dtype": "bfloat16",
-    "transformers_version": "4.44.2",
+    "transformers_version": "4.57.3",
     "use_cache": true,
     "v_head_dim": 128,
     "vocab_size": 129280
-}
+}

mergekit_config.yml

@@ -0,0 +1,21 @@
+base_model: /root/myCodeLab/host/downloads/models/40Bra
+dtype: float32
+merge_method: dare_linear
+modules:
+  default:
+    slices:
+    - sources:
+      - layer_range: [0, 40]
+        model: /root/myCodeLab/host/downloads/models/40Bra
+      - layer_range: [0, 40]
+        model: /root/myCodeLab/host/verl/ckpts/40bra_k8s_single_domain/40bra_k8s_16node_sd_c362_20260327_205644_unknown/global_step_50/actor/huggingface
+        parameters:
+          density: 1.0
+          weight:
+          - filter: .mlp.gate.
+            value: 0.0
+          - value: 0.5
+    - sources:
+      - layer_range: [40, 41]
+        model: /root/myCodeLab/host/downloads/models/40Bra
+out_dtype: bfloat16

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