初始化项目，由ModelHub XC社区提供模型

Model: EleutherAI/GPT-2-wikitext-chunks
Source: Original Platform

README.md

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+---
+license: mit
+base_model: gpt2
+datasets:
+- EleutherAI/bergson-wikitext-512-chunks
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# GPT-2 fine-tuned on bergson-wikitext-512-chunks
+
+GPT-2 (124M) fine-tuned on [EleutherAI/bergson-wikitext-512-chunks](https://huggingface.co/datasets/EleutherAI/bergson-wikitext-512-chunks)
+(WikiText-2 pre-chunked to 512-token sequences, 4,608 train chunks) using the
+[bergson](https://github.com/EleutherAI/bergson) MAGIC trainer, as the trained
+model for MAGIC attribution experiments.
+
+## Training
+
+- 4 epochs, global batch size 64 (8x data parallel), 288 steps
+- AdamW, polynomial LR schedule: lr 8e-4 (start 1e-6, end 8e-5), 25% warmup, fp32
+- Loss on held-out `test[:4]` chunks: 3.22 (base gpt2: 3.62)
+
+## Files
+
+- Standard HF model + tokenizer files
+- `bergson_config.yaml` — the fully-resolved bergson run config (all fields incl. defaults) that produced this model; rerun with `python -m bergson bergson_config.yaml`
+- `optimizer.pt` — AdamW second moments (`exp_avg_sq`) at the final training
+  step, in bergson's `optimizer.pt` normalizer format
+  (`{"state": {idx: {"exp_avg_sq": ...}}, "param_groups": [...]}` with `idx`
+  indexing deduplicated `model.named_parameters()`), for gradient
+  normalization in attribution runs.