AnonymousForReview2
/

script_poly_5_medium_Llama-3.1-8B_r8_mlp

@@ -1,206 +1,58 @@
 ---
 base_model: meta-llama/Llama-3.1-8B
-library_name: peft
 tags:
-- base_model:adapter:meta-llama/Llama-3.1-8B
-- lora
-- transformers
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.17.1

 ---
+license: apache-2.0
 base_model: meta-llama/Llama-3.1-8B
 tags:
+  - lora
+  - peft
+  - polynomial
+  - regression
+  - grokking
 ---
+# LoRA adapter: poly_5_medium on meta-llama/Llama-3.1-8B
+Rank-8 **MLP-only LoRA** adapter for **polynomial regression (sequence classification head)** on the `poly_5_medium` polynomial dataset.
+## Adapter config
+| Setting     | Value |
+|------------|-------|
+| Base model | `meta-llama/Llama-3.1-8B` |
+| Polynomial | `poly_5_medium` |
+| LoRA rank  | 8 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.05 |
+| Target     | MLP layers only (e.g. `gate_proj`, `up_proj`, `down_proj` for LLaMA) |
+## How to load and run
+From this repo (requires `transformers`, `peft`, `torch`):
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from peft import PeftModel
+import torch
+base_model_id = "meta-llama/Llama-3.1-8B"
+adapter_repo_id = "AnonymousForReview2/script_poly_5_medium_Llama-3.1-8B_r8_mlp"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base = AutoModelForSequenceClassification.from_pretrained(
+    base_model_id, num_labels=1, problem_type="regression"
+)
+model = PeftModel.from_pretrained(base, adapter_repo_id)
+model.eval()
+# Example: predict for input vector
+text = "input: [1.0, 2.0, 3.0, 4.0] target:"
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    out = model(**inputs).logits.item()
+print(out)
+```
+Or use the provided script from the project root:
+```bash
+python huggingface/load_from_hub.py --repo_id AnonymousForReview2/script_poly_5_medium_Llama-3.1-8B_r8_mlp
+```