Qwen3p5 397b Counterfactual E1 Kl0

--- base_model: Qwen/Qwen3.5-397B-A17B library_name: peft pipeline_tag: text-generation license: apache-2.0 tags: - base_model:adapter:Qwen/Qwen3.5-397B-A17B - lora - transformers - chive --- # qwen3p5_397b_counterfactual_e1_kl0 Qwen3.5-397B-A17B counterfactual-prediction (binary Yes/No) model. Trained with Tinker (LoRA rank 64). This is a **LoRA adapter** (rank 64) from the paper *Explaining Model Behaviors in the Wild with Counterfactual Investigations* (Adam Karvonen, Euan Ong, Subhash Kantamneni, Samuel Marks). - **Base model:** `Qwen/Qwen3.5-397B-A17B` - **Adapter type:** LoRA (PEFT), rank 64 - **Code:** https://github.com/adamkarvonen/chive - **Dataset:** https://huggingface.co/datasets/adamkarvonen/chive-data ## Usage ```python from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-397B-A17B", torch_dtype="auto", device_map="auto") model = PeftModel.from_pretrained(base, "adamkarvonen/qwen3p5_397b_counterfactual_e1_kl0") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-397B-A17B") ``` ## License This LoRA adapter is a derivative of `Qwen/Qwen3.5-397B-A17B` and is released under the Apache 2.0 license. Its training data is derived from multiple upstream sources with their own terms — see the [dataset card](https://huggingface.co/datasets/adamkarvonen/chive-data) for the full license/attribution table (WildChat is ODC-BY and requires attribution). ### Framework versions - PEFT 0.19.1

--- base_model: Qwen/Qwen3.5-397B-A17B library_name: peft pipeline_tag: text-generation license: apache-2.0 tags: - base_model:adapter:Qwen/Qwen3.5-397B-A17B - lora - transformers - counterfactual-investigations --- # qwen3p5_397b_counterfactual_e1_kl0 Qwen3.5-397B-A17B counterfactual-prediction (binary Yes/No) model. Trained with Tinker (LoRA rank 64). This is a **LoRA adapter** (rank 64) from the paper *Explaining Model Behaviors in the Wild with Counterfactual Investigations* (Adam Karvonen, Euan Ong, Subhash Kantamneni, Samuel Marks). - **Base model:** `Qwen/Qwen3.5-397B-A17B` - **Adapter type:** LoRA (PEFT), rank 64 - **Code:** https://github.com/adamkarvonen/counterfactual-investigations - **Dataset:** https://huggingface.co/datasets/adamkarvonen/counterfactual-investigations-data ## Usage ```python from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-397B-A17B", torch_dtype="auto", device_map="auto") model = PeftModel.from_pretrained(base, "adamkarvonen/qwen3p5_397b_counterfactual_e1_kl0") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-397B-A17B") ``` ## License This LoRA adapter is a derivative of `Qwen/Qwen3.5-397B-A17B` and is released under the Apache 2.0 license. Its training data is derived from multiple upstream sources with their own terms — see the [dataset card](https://huggingface.co/datasets/adamkarvonen/counterfactual-investigations-data) for the full license/attribution table (WildChat is ODC-BY and requires attribution). ### Framework versions - PEFT 0.19.1

Source context: 19 downloads · 0 likes · Pipeline text-generation · Library peft · Repo adamkarvonen/qwen3p5_397b_counterfactual_e1_kl0