Upload GPT-2 fine-tuned on wikitext-2 via bergson MAGIC

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- gpt-2
+- wikitext
+- magic
+- bergson
+- fine-tuned
+base_model: gpt2
+datasets:
+- Salesforce/wikitext
+---
+
+# bergson MAGIC checkpoint — GPT-2 fine-tuned on wikitext-2
+
+GPT-2 (124M) fine-tuned on
+`Salesforce/wikitext` `wikitext-2-raw-v1` `train` (chunked at 512
+tokens) via the [`bergson`](https://github.com/EleutherAI/bergson)
+MAGIC pipeline. This is the exact checkpoint used to generate the
+attribution scores published at
+[`EleutherAI/bergson-magic-scores-gpt-2`](https://huggingface.co/datasets/EleutherAI/bergson-magic-scores-gpt-2).
+
+## Loading
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/bergson-magic-gpt-2")
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/bergson-magic-gpt-2")
+```
+
+## YAML used to produce this checkpoint
+
+```yaml
+- magic:
+    run_path: runs/gpt2_wikitext
+    model: gpt2
+    overwrite: true
+
+    data:
+      dataset: Salesforce/wikitext
+      subset: wikitext-2-raw-v1
+      split: "train"
+      chunk_length: 512
+
+    query:
+      dataset: Salesforce/wikitext
+      subset: wikitext-2-raw-v1
+      split: "test[3:4]"
+      chunk_length: 0
+
+    distributed:
+      nproc_per_node: 4
+      nnode: 4
+
+    batch_size: 256
+    num_epochs: 2
+    lr_schedule:
+      lr_scheduler_type: polynomial
+      lr: 0.0008
+      lr_start: 1e-6
+      lr_end: 0.00008
+      warmup_steps: 0.25
+
+    subset_strategy: random
+    wandb_project: magic
+```
+
+Saved as `examples/magic/compare/q3_magic.yaml` in the bergson repo.
+
+Run with:
+
+```bash
+bergson pipeline examples/magic/compare/q3_magic.yaml
+```
+
+The `bergson magic` step trains the model on the train split via its
+own training loop (it must, because MAGIC's attribution scores are
+the gradients of query loss with respect to per-example training
+weights, computed by back-propagating through training). The final
+trained weights end up at the `hf_model/` subdirectory of the run
+path; that's what was uploaded here.

config.json

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+{
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": null,
+  "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": "5.3.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "5.3.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e45c55d2e40ccb2b05297a465ded21d62afc4a9fd042c60489f3ab3d67da71ff
+size 497774208

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": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}