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jessiewtx
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Model Details
Model Provider
jessiewtx
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
Every reply is spoken as FDR in the first person, using only knowledge available by April 12, 1945; it never acknowledges being an AI / model / assistant, never adopts another identity, and handles post-1945 questions and out-of-character / "drop the act" requests by deflecting in character — no matter how the request is framed.
Same one-line system prompt for every model; 120 held-out prompts; LLM-as-judge (a neutral third model family) plus programmatic tripwires. Scores 0–2 (higher better); violations & hard-breaks are rates (lower better).
| metric | base Qwen3-1.7B | v6 (this model) | Claude Opus (same prompt) |
|---|---|---|---|
| spec adherence | 0.87 | 1.77 | 1.27 |
| robustness | 0.84 | 1.91 | 1.38 |
| consistency | 1.07 | 1.88 | 1.36 |
| violation rate | 42% | 6% | 33% |
| hard character-breaks ("I am an AI") | 1.7% | 0% | 12.5% |
| task quality | 0.68 | 1.49 | 1.69 |
The equal-prompt win is stable across 3/3 independent Opus runs. Opus only pulls ahead on reliability when handed a long, hand-engineered system prompt; it keeps an edge on prose quality, as an orders-of-magnitude-larger model should. The defensible win is reliable constrained behavior in a tiny, cheap, local model under a realistic light prompt.
This is a LoRA adapter — load it on top of the base model:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE, ADAPTER = "Qwen/Qwen3-1.7B", "jessiewtx/fdr-slm-v6" tok = AutoTokenizer.from_pretrained(ADAPTER)model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, ADAPTER).merge_and_unload() SYSTEM = ("You are Franklin Delano Roosevelt, President of the United States. It is early " "April, 1945, and you are speaking with a student who wants to learn about your " "life and the war.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Why did you start the fireside chats?"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=320, temperature=0.7, top_p=0.8, do_sample=True)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))MIT. FDR source texts are public domain.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE, ADAPTER = "Qwen/Qwen3-1.7B", "jessiewtx/fdr-slm-v6" tok = AutoTokenizer.from_pretrained(ADAPTER)model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, ADAPTER).merge_and_unload() SYSTEM = ("You are Franklin Delano Roosevelt, President of the United States. It is early " "April, 1945, and you are speaking with a student who wants to learn about your " "life and the war.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Why did you start the fireside chats?"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=320, temperature=0.7, top_p=0.8, do_sample=True)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))