Alex6657/AgentTailor-BookCrossing

AgentTailor BookCrossing adapter

Author-supplied fine-tuned checkpoint for the BookCrossing setting of the AgentTailor project. The original checkpoint directory is named ReFICR_qlora_bc. This release preserves the adapter weights byte-for-byte and replaces the training machine's base-model path with the public GritLM/GritLM-7B identifier.

This is a LoRA adapter, requiring the separate GritLM-7B base model. It is not a standalone merged model or the Stage-2 LLM agent. Use the AgentTailor code for candidate preparation, agent scoring, evidence merging and evaluation.

Checkpoint details

• Base model: GritLM/GritLM-7B
• LoRA rank: 64; alpha: 32; dropout: 0.05; bias: none
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• 448 finite tensors, 167,772,160 adapter parameters
• Adapter file: 671,149,168 bytes
• SHA-256: 1dd900ae9752af6105ded55c67c1b53f8c67b3a4d97ad5bce113d7a86f8414e0

The source folder is labeled QLoRA. The exported adapter itself is stored as ordinary safetensors; this does not imply that the file contains a quantized copy of the complete base model. The original auxiliary non-LoRA state dict was inspected using weights_only=True and is empty, so it is omitted. training_base_config.json records the supplied architecture for provenance; load the base architecture and implementation from the base model repository.

Load with GritLM and PEFT

Install the project's GritLM fine-tuning dependencies, including PyTorch, Transformers, PEFT, Accelerate and GritLM, in a suitable GPU environment. Use a local checkout of this adapter as adapter_path, or its Hugging Face repository ID after publication:

from gritlm import GritLM
from peft import PeftModel

adapter_path = './AgentTailor-BookCrossing'
model = GritLM('GritLM/GritLM-7B', mode='embedding', torch_dtype='auto')
model.model = PeftModel.from_pretrained(model.model, adapter_path).merge_and_unload()
model.model.eval()
embeddings = model.encode(['Example item description'], instruction='<|embed|>\n')

The adapter must actually be applied before encoding. Match the project's profile/item formatting, instruction prefixes, pooling and attention settings for experiment reproduction. Generic generation alone does not reproduce the recommendation pipeline.

Validation and scope

The release checks all adapter tensors for finite values, verifies the expected 32-layer LoRA coverage, loads the tokenizer locally, and checks copied weights against SHA-256. Full GritLM-7B inference and benchmark reproduction have not been run as part of this packaging step; no new performance claims are made. Training logs, raw datasets and private machine paths are not distributed.

Attribution

The code builds on GritLM and the supplied ReFICR fine-tuning implementation. Consult the base model card for its Apache-2.0 license and attribution. This card does not assign a new license to the author-supplied adapter; no separate adapter license was supplied.