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AmericanEagle
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Model Details
Model Provider
AmericanEagle
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-1.5B-Instructr=16, alpha 32, dropout 0.05)AmericanEagle/personabench-aikopython
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = "Qwen/Qwen2.5-1.5B-Instruct"adapter = "AmericanEagle/personabench-aiko-qwen2.5-1.5b-lora"tok = AutoTokenizer.from_pretrained(base)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base), adapter)
See the project repository for controlled benchmarks, raw responses, and limitations.
On 20 scenario-held-out prompts, GLM-5.2 judging scored the minimal-prompt LoRA at 2.06/5 and LoRA plus the full persona prompt at 2.17/5. The same base model with the full prompt scored 1.26/5; few-shot prompting scored 1.56/5. GLM-5.2 as an external reference scored 4.65/5. See the live results dashboard and the repository for raw responses and caveats.