| ---
|
| library_name: peft
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| base_model: GritLM/GritLM-7B
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| base_model_relation: adapter
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| tags:
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| - lora
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| - recommendation
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| - agenttailor
|
| ---
|
|
|
| # AgentTailor BookCrossing adapter
|
|
|
| Author-supplied fine-tuned checkpoint for the BookCrossing setting of the
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| [AgentTailor project](https://github.com/Alex-bupt/AgentTailor).
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| The original checkpoint directory is named `ReFICR_qlora_bc`. This release
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| preserves the adapter weights byte-for-byte and replaces the training machine's
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| base-model path with the public `GritLM/GritLM-7B` identifier.
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|
|
| This is a LoRA adapter, requiring the separate GritLM-7B base model. It is not
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| a standalone merged model or the Stage-2 LLM agent. Use the AgentTailor code
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| for candidate preparation, agent scoring, evidence merging and evaluation.
|
|
|
| ## Checkpoint details
|
|
|
| - Base model: [GritLM/GritLM-7B](https://huggingface.co/GritLM/GritLM-7B)
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| - LoRA rank: 64; alpha: 32; dropout: 0.05; bias: none
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| - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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| - 448 finite tensors, 167,772,160 adapter parameters
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| - Adapter file: 671,149,168 bytes
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| - SHA-256: `1dd900ae9752af6105ded55c67c1b53f8c67b3a4d97ad5bce113d7a86f8414e0`
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|
|
| 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
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| copy of the complete base model. The original auxiliary non-LoRA state dict
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| was inspected using `weights_only=True` and is empty, so it is omitted.
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| `training_base_config.json` records the supplied architecture for provenance;
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| load the base architecture and implementation from the base model repository.
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|
|
| ## Load with GritLM and PEFT
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|
|
| Install the project's GritLM fine-tuning dependencies, including PyTorch,
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| Transformers, PEFT, Accelerate and GritLM, in a suitable GPU environment.
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| Use a local checkout of this adapter as `adapter_path`, or its Hugging Face
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| repository ID after publication:
|
|
|
| ```python
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| from gritlm import GritLM
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| from peft import PeftModel
|
|
|
| adapter_path = './AgentTailor-BookCrossing'
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| model = GritLM('GritLM/GritLM-7B', mode='embedding', torch_dtype='auto')
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| model.model = PeftModel.from_pretrained(model.model, adapter_path).merge_and_unload()
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| model.model.eval()
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| embeddings = model.encode(['Example item description'], instruction='<|embed|>\n')
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| ```
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|
|
| The adapter must actually be applied before encoding. Match the project's
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| profile/item formatting, instruction prefixes, pooling and attention settings
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| for experiment reproduction. Generic generation alone does not reproduce the
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| recommendation pipeline.
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|
|
| ## Validation and scope
|
|
|
| The release checks all adapter tensors for finite values, verifies the expected
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| 32-layer LoRA coverage, loads the tokenizer locally, and checks copied weights
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| 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.
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| Training logs, raw datasets and private machine paths are not distributed.
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|
|
| ## Attribution
|
|
|
| The code builds on GritLM and the supplied ReFICR fine-tuning implementation.
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| Consult the [base model card](https://huggingface.co/GritLM/GritLM-7B) for its
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| Apache-2.0 license and attribution. This card does not assign a new license to
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| the author-supplied adapter; no separate adapter license was supplied.
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|
|
