GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
sjsim
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
sjsim
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
The goal is to test how far a small, locally-runnable open-source model can be pushed with cheap, lightweight training. Instead of calling a frontier API, can a 14B model that runs on one 16GB consumer GPU be given the reasoning style of a specific expert? That means the way they frame a problem, the priors they argue from, and how they handle uncertainty.
Lee Kuan Yew is a good test case. His reasoning method is distinctive and well-documented, and there is a large public record to learn from. The result is a roughly 1GB adapter, trained overnight on one consumer GPU for a few dollars of API cost, that measurably shifts the base model toward that method (see Evaluation below). The pipeline is a reusable template. Swap the source material and you can teach any well-documented expert's reasoning to a model you own and run offline. It is fully open source.
Research into style transfer and historical-persona modelling, and educational exploration of a reasoning style. Not for impersonation, and not for presenting outputs as authentic statements by the real person.
Judged by claude-opus-4-8 on 24 held-out interview questions that none of the
models trained on. Six reasoning moves, plus a 1 to 5 voice score:
| reasoning move | base Qwen3-14B | this adapter (epoch-2) | epoch-3 |
|---|---|---|---|
| reframes the question | 29% | 38% | 29% |
| reasons from first principles | 50% | 54% | 62% |
| concrete historical analogy | 0% | 25% | 38% |
| interests over sentiment | 75% | 88% | 79% |
| bounds its uncertainty | 8% | 33% | 17% |
| blunt and direct | 46% | 88% | 88% |
| voice (1 to 5) | 2.04 | 2.88 | 2.96 |
This is the epoch-2 checkpoint. A fully-trained epoch-3 adapter reached a lower training loss but overfit. On unseen questions it lost half its "bounds its uncertainty" and its question-reframing, because it had memorised his confident, declarative register. Epoch-2 generalises better and ties epoch-3 on voice. Note the eval is n=24, so the smaller per-move numbers are directional.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigimport torch tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-14B", quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16), device_map={"": 0})model = PeftModel.from_pretrained(base, "sjsim/lky-qlora") msgs = [ {"role": "system", "content": "You are Lee Kuan Yew, Prime Minister of " "Singapore, speaking candidly in an interview. It is August 1965."}, {"role": "user", "content": "Will Singapore survive as an independent nation?"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(prompt, return_tensors="pt").to("cuda")out = model.generate(**ids, max_new_tokens=500, do_sample=True, temperature=0.8, top_p=0.9)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))Keep enable_thinking=False. The style lives in the visible answer.
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigimport torch tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-14B", quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16), device_map={"": 0})model = PeftModel.from_pretrained(base, "sjsim/lky-qlora") msgs = [ {"role": "system", "content": "You are Lee Kuan Yew, Prime Minister of " "Singapore, speaking candidly in an interview. It is August 1965."}, {"role": "user", "content": "Will Singapore survive as an independent nation?"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(prompt, return_tensors="pt").to("cuda")out = model.generate(**ids, max_new_tokens=500, do_sample=True, temperature=0.8, top_p=0.9)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))