初始化项目，由ModelHub XC社区提供模型

Model: Marcoson320/codeparrot-gpt2-mi50
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+- gpt2
+- code
+- mi50
+- amd
+- rocm
+datasets:
+- huggingface-course/codeparrot-ds-train
+language:
+- code
+pipeline_tag: text-generation
+---
+
+# codeparrot-gpt2-mi50
+
+GPT-2 small (124M 參數) 之 causal language model，依 HuggingFace LLM Course Chapter 7.6 所述方法訓練。權重自隨機初始化開始，於 codeparrot-ds 資料集上訓練 1 epoch，作為 from-scratch 訓練流程在 AMD MI50 平台之可重現紀錄。
+
+## 訓練配置
+
+| 項目 | 值 |
+|---|---|
+| 模型架構 | GPT-2 (n_layer=12, n_head=12, n_embd=768) |
+| 參數量 | 124,242,432 |
+| Tokenizer | huggingface-course/code-search-net-tokenizer (BPE, vocab=50,000) |
+| Context length | 128 tokens |
+| 訓練集 | huggingface-course/codeparrot-ds-train (16,702,061 length-128 chunks) |
+| 驗證集 | huggingface-course/codeparrot-ds-valid |
+| Optimizer | AdamW (β₁=0.9, β₂=0.999, weight_decay=0.1) |
+| Learning rate | 5×10⁻⁴, cosine schedule, warmup 1,000 steps |
+| Effective batch size | 256 (per_device_bs=64 × grad_accum=2 × world_size=2) |
+| Precision | fp16 |
+| 平行化 | DistributedDataParallel (DDP), NCCL/RCCL backend |
+| 總步數 | 65,243 (1 epoch) |
+| Eval / save 間隔 | 每 5,000 steps |
+
+## 硬體環境
+
+- GPU：2 × AMD Radeon Instinct MI50 (32 GB HBM2 each, gfx906)
+- 平台：PyTorch + ROCm，容器化部署
+- 訓練時間：約 19 小時
+- 平均 throughput：159.8 samples/sec, ~1.41 sec/step
+
+## Loss 與訓練動力學
+
+每 5,000 steps 取一個訓練 metrics 與 eval metrics 紀錄。
+
+| step | epoch | learning_rate | train_loss | grad_norm | eval_loss |
+|---:|---:|---:|---:|---:|---:|
+| 5,000  | 0.077 | 4.952×10⁻⁴ | 2.677 | 0.180 | 1.752 |
+| 10,000 | 0.153 | 4.762×10⁻⁴ | 1.685 | 0.152 | 1.520 |
+| 15,000 | 0.230 | 4.437×10⁻⁴ | 1.529 | 0.153 | 1.415 |
+| 20,000 | 0.307 | 3.996×10⁻⁴ | 1.447 | 0.145 | 1.347 |
+| 25,000 | 0.383 | 3.467×10⁻⁴ | 1.386 | 0.154 | 1.295 |
+| 30,000 | 0.460 | 2.880×10⁻⁴ | 1.334 | 0.160 | 1.247 |
+| 35,000 | 0.537 | 2.271×10⁻⁴ | 1.288 | 0.160 | 1.204 |
+| 40,000 | 0.613 | 1.675×10⁻⁴ | 1.241 | 0.170 | 1.160 |
+| 45,000 | 0.690 | 1.128×10⁻⁴ | 1.200 | 0.175 | 1.123 |
+| 50,000 | 0.766 | 6.631×10⁻⁵ | 1.162 | 0.174 | 1.090 |
+| 55,000 | 0.843 | 3.072×10⁻⁵ | 1.135 | 0.180 | 1.066 |
+| 60,000 | 0.920 | 8.175×10⁻⁶ | 1.113 | 0.191 | 1.054 |
+| 65,000 | 0.996 | 1.78×10⁻⁸  | 1.106 | 0.180 | 1.051 |
+
+訓練未進行更多 epoch 或超參數搜尋。後半段 cosine 衰減使 lr 趨近於零，gradient norm 維持在 0.15-0.19 區間，未出現發散或不穩定徵兆。
+
+## 使用
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model="Marcoson320/codeparrot-gpt2-mi50",
+    device=0,
+)
+
+print(pipe("# scatter plot of x, y\n", max_new_tokens=64)[0]["generated_text"])
+```
+
+## 限制
+
+- Context 上限 128 tokens，無法處理較長之程式碼段落。
+- 訓練資料偏重 pandas / sklearn / matplotlib / seaborn 之 GitHub Python，其他領域之程式碼覆蓋有限。
+- 模型容量小，續寫易出現 repetition；推論時可設 `repetition_penalty>1.0` 或 `no_repeat_ngram_size` 緩解。

config.json

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+{
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 0,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 128,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 0,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 50000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": [
+    0
+  ],
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.5.0",
+  "use_cache": true
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_args.bin

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