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Model: stech2333/brainalign-qwen2.5-1.5b-C
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+license: apache-2.0
+tags:
+- transformers
+- safetensors
+- text-generation
+- qwen2
+- lora
+- merged-adapter
+- brainalign
+---
+
+# C
+
+## Summary
+
+This model is the `C` BrainAlign stage-2 LoRA checkpoint (`best_by_retrieval`) merged into the full base model `Qwen/Qwen2.5-1.5B-Instruct`.
+
+The folder is saved in standard Hugging Face format so it can be uploaded directly for Open LLM Leaderboard v2 evaluation.
+
+## Model Details
+
+- Repository: `stech2333/brainalign-qwen2.5-1.5b-C`
+- Architecture: `Qwen2ForCausalLM`
+- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
+- Format: merged full-weights Hugging Face Transformers checkpoint
+- Precision for leaderboard submission: `bfloat16`
+
+## Intended Use
+
+This model is intended for research and evaluation of the BrainAlign stage-2 fine-tuning branch `C`. It is suitable for standard Hugging Face `transformers` loading and for leaderboard-style offline evaluation where a full model repository is required instead of a standalone LoRA adapter.
+
+## Export Metadata
+
+- Export time: `2026-05-14T19:55:09`
+- Model kind: `merged_lora`
+- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
+- Checkpoint choice: `best_by_retrieval`
+- Projector branch: `C`
+- Stage-1 head: `mean5_pca1024_contrastive_seed42`
+
+## License
+
+This merged checkpoint is distributed under `apache-2.0`, following the declared license metadata in this repository. Please also review the upstream base model card for any additional usage notes from `Qwen/Qwen2.5-1.5B-Instruct`.
+
+## Limitations
+
+This repository documents packaging and export details for evaluation. It does not claim additional safety alignment or benchmark superiority beyond the fine-tuning performed in the BrainAlign project. Downstream behavior should be validated on the target tasks before real use.
+
+## Loading
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("C")
+tokenizer = AutoTokenizer.from_pretrained("C")
+```