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Model: Flink-ddd/MoE-Pilot-Align-2.7B
Source: Original Platform

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LICENSE

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+Tongyi Qianwen LICENSE AGREEMENT
+
+Tongyi Qianwen Release Date: August 3, 2023
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README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- moe
+- alignment
+license: apache-2.0
+library_name: transformers
+---
+
+# MoE-Pilot-Align-2.7B
+
+## Overview
+
+**MoE-Pilot-Align-2.7B** is a high-performance Sparse Mixture-of-Experts (MoE) model (14.3B total / 2.7B active), meticulously adapted and fine-tuned on a standard **8-GPU SXM node**. 
+
+This project is not a conventional fine-tuning exercise; it is an Infrastructure-Level Alignment showcase. It validates a production-ready pipeline for scaling MoE architectures by resolving critical bottlenecks in distributed initialization, heterogeneous memory management, and collective communication.
+
+## The Engine: [Kernel-Align](https://github.com/Flink-ddd/Kernel-Align)
+To achieve maximum throughput and memory efficiency during the alignment phase, this model is powered by **Kernel-Align**, an extreme post-training infrastructure optimized for NVIDIA and AMD GPUs.
+
+* **Breaking the Memory Wall**: Optimized for **GRPO (Group Relative Policy Optimization)**, enabling a Group Size of **G=256** on a single A100 by keeping additional VRAM usage constant (**~0.5GB**).
+* **Extreme Sampling Latency**: Integrated with **FlashInfer** and custom fused kernels, achieving up to **399x speedup** in the rollout phase compared to native PyTorch.
+* **Universal Backend**: Native support for **AMD (ROCm/AITER)** and NVIDIA (CUDA), ensuring seamless cross-platform performance.
+
+## Engineering & Infrastructure Innovations
+
+### 1. Zero-Deadlock AOT Kernel Orchestration
+In high-density 8-GPU environments, simultaneous JIT (Just-in-Time) compilation of MoE fused kernels often triggers distributed deadlocks due to I/O race conditions during the NCCL handshake.
+- Innovation: Implemented an Ahead-of-Time compilation orchestrator. By decoupling CUDA/ROCm kernel building from the training lifecycle, we achieved zero-latency "Cold Starts" and eliminated synchronization timeouts.
+
+### 2. Reflection-Based Parameter Metadata Injection
+Standard DeepSpeed-ZeRO engines require precise expert-group tagging to avoid `AssertionError` during state partitioning. Hard-coded tagging is fragile and lacks portability.
+- Solution: Developed a Reflection-based Metadata Injector. At the optimizer setup stage, the system dynamically scans tensors for `allreduce` attributes and naming patterns to inject `moe: True` tags.
+- Impact: Ensured seamless compatibility between the MoE expert layers and the ZeRO-1/2 optimizer state sharding, maximizing VRAM efficiency on the 80GB A100 footprint.
+
+### 3. Topology-Aware Parallelism (EP8)
+- Parallelism Strategy: Optimized for a Single-Node 8-GPU Full-Mesh topology using **Expert Parallelism (EP=8)**.
+- Communication Tuning: Refactored the `All-to-All` collective primitives to match the NVLink 3.0 bi-directional bandwidth. By balancing the load across 32 total experts (4 experts per GPU), we maintained a stable throughput of 185+ TFLOPs per device.
+
+## Model Specifications
+| Feature | Configuration |
+| :--- | :--- |
+| **Architecture** | Sparse MoE (Decoder-only) |
+| **Total Parameters** | 14.3 Billion |
+| **Active Parameters** | 2.7 Billion |
+| **Experts Count** | 32 (Total) |
+| **Routing Strategy** | Top-2 Gating with Load Balancing |
+| **Precision** | Mixed Precision (BF16-O2) |
+
+## Hardware & Software Environment
+- **Compute**: 1 x Node | 8 x NVIDIA A100 80GB SXM
+- **Interconnect**: NVLink 3.0 (600 GB/s Full-Mesh)
+- **Framework**: Custom Fork of **Megatron-DeepSpeed**
+- **CUDA/ROCm Compatibility**: Validated on CUDA 12.4; Architectural design supports seamless porting to AMD MI300X/ROCm environments via RCCL optimization.
+
+---
+
+## Community Quantizations
+Special thanks to [@mradermacher](https://huggingface.co/mradermacher) for providing GGUF weights:
+- [GGUF Weights (Q2_K to Q8_0)](https://huggingface.co/mradermacher/MoE-Pilot-Align-2.7B-GGUF)
+- 
+*This repository serves as a technical benchmark for implementing production-scale MoE alignment protocols on next-generation heterogeneous compute clusters.*

config.json

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+{
+  "architectures": [
+    "Qwen2MoeForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 8192,
+  "max_window_layers": 21,
+  "model_type": "qwen2_moe",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 16,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.0.dev0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "decoder_sparse_step": 1,
+  "moe_intermediate_size": 1408,
+  "shared_expert_intermediate_size": 5632,
+  "num_experts_per_tok": 4,
+  "num_experts": 60,
+  "norm_topk_prob": false,
+  "output_router_logits": false,
+  "router_aux_loss_coef": 0.001
+}

configuration.json

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+{
+    "framework": "pytorch",
+    "task": "fill-mask",
+    "allow_remote": true
+}

generation_config.json

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+  "pad_token_id": 151643,
+  "eos_token_id": [
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+  ],
+  "transformers_version": "4.39.0.dev0"
+}

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tokenizer_config.json

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+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
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