python api + remove generate_multi_lora + rename config path + test eval scripts

examples/python_api.py

@@ -0,0 +1,43 @@
+# caveat: this interface only supports non-batched inputs
+# for batched inference please see `src/ctx_to_lora/modeling/hypernet.py`
+import torch
+
+from ctx_to_lora.model_loading import get_tokenizer
+from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
+
+checkpoint_path = "../../ctx-to-lora/train_outputs/runs/Sep29_14-42-46_slurm0-a3nodeset-9_88483_1e7bb34e/checkpoint-40000/pytorch_model.bin"
+state_dict = torch.load(checkpoint_path, weights_only=False)
+model = ModulatedPretrainedModel.from_state_dict(
+    state_dict, train=False, use_sequence_packing=False
+)
+model.reset()
+
+tokenizer = get_tokenizer(model.base_model.name_or_path)
+
+doc = open("data/sakana_wiki.txt").read()
+chat = [{"role": "user", "content": "Summarize what Sakana AI does."}]
+chat_ids = tokenizer.apply_chat_template(
+    chat,
+    add_special_tokens=False,
+    return_attention_mask=False,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+
+
+outputs = model.generate(input_ids=chat_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+
+
+# calls after internalization will be influenced by internalized info
+model.internalize(doc)
+
+outputs = model.generate(input_ids=chat_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+
+
+# remove internalized info
+model.reset()
+
+outputs = model.generate(input_ids=chat_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))