Upload README.md with huggingface_hub

README.md

@@ -1,63 +1,98 @@
 ---
-base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
-library_name: peft
-model_name: ces_phase3a_lora
+license: mit
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 tags:
-- base_model:adapter:unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+- llama
 - lora
-- sft
-- transformers
-- trl
+- political-science
+- survey-replication
+- canadian-election-study
+- peft
 - unsloth
-licence: license
+datasets:
+- custom
+language:
+- en
 pipeline_tag: text-generation
 ---
 
-# Model Card for ces_phase3a_lora
+# CES Phase 3A LoRA: Leader Affect + Policy Positions
 
-This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This is the **recommended** model for predicting political ideology from demographics, leader thermometers, and wedge issues.
 
-## Quick start
+## Performance
 
-```python
-from transformers import pipeline
+| Model | Variables | r |
+|-------|-----------|---|
+| **Phase 3A (this model)** | Demographics + Leader Ratings + Wedge Issues | **0.560** |
+| Phase 3B | Same + Party ID | 0.574 |
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+**Partisan Delta = 0.014** (essentially zero)
+
+## Key Finding: "The Null Result of the Label"
+
+Adding party identification provides almost no improvement (+1.4%) over leader affect and policy positions alone.
 
-## Training procedure
+**What this means:**
+- Party identity is **redundant** — it's already encoded in how people feel about leaders and their policy positions
+- Canadian ideology is **substantive, not tribal** — people's "team" reflects their actual views
+- **Phase 3A is the preferred model** — predicts ideology without "cheating" by asking party affiliation
 
- 
+## Variables
 
+### Demographics
+Age, gender, province, education, employment, religion, marital status, urban/rural, born in Canada
 
-This model was trained with SFT.
+### Leader Thermometers (0-100 ratings)
+- Justin Trudeau
+- Erin O'Toole
+- Jagmeet Singh
 
-### Framework versions
+### Wedge Issues
+- Carbon tax support
+- Energy sector/pipelines
+- Medical assistance in dying
 
-- PEFT 0.18.0
-- TRL: 0.24.0
-- Transformers: 4.57.2
-- Pytorch: 2.9.1
-- Datasets: 4.3.0
-- Tokenizers: 0.22.1
+## Usage
 
-## Citations
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Meta-Llama-3.1-8B-Instruct",
+    load_in_4bit=True
+)
+model = PeftModel.from_pretrained(base_model, "baglecake/ces-phase3a-lora")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
+```
 
+## Training Details
 
+- **Base model**: meta-llama/Meta-Llama-3.1-8B-Instruct (4-bit quantized via Unsloth)
+- **Training data**: ~14,450 examples from CES 2021
+- **LoRA rank**: 32
+- **LoRA alpha**: 64
+- **Epochs**: 3
+- **Hardware**: NVIDIA A100 40GB (Colab Pro)
+
+## Limitations
+
+1. **Narrow task**: Model only outputs ideology numbers (0-10).
+2. **Canadian-specific**: Trained on CES 2021 under Trudeau government.
+3. **Leader-specific**: Uses 2021 leader names.
+
+## Citation
 
-Cite TRL as:
-    
 ```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
+@software{ces-phase3-lora,
+  title = {CES Phase 3 LoRA: Leader Affect and Policy Prediction},
+  author = {Coburn, Del},
+  year = {2025},
+  url = {https://huggingface.co/baglecake/ces-phase3a-lora}
 }
-```
+```
+
+## Part of emile-GCE
+
+This model is part of the [emile-GCE](https://github.com/delcoburn/emile-gce) project for Generative Computational Ethnography.