leviathune

persona-0.1

README

License: apache-2.0

How to use

This is an adapter, not a full model. Load the base model and apply the adapter with PEFT:

python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(base, dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "leviathune/persona-0.1")
tokenizer = AutoTokenizer.from_pretrained("leviathune/persona-0.1")

messages = [{"role": "user", "content": "My brother keeps borrowing money and never pays it back. What should I do?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
out = model.generate(**inputs, max_new_tokens=800, temperature=0.7, top_p=0.95, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

No system prompt is needed or expected; the adapter was trained and sampled without one. To fold the adapter into the base weights, call model.merge_and_unload() and save.

Earlier epochs are under checkpoints/ (checkpoints/epoch1, checkpoints/epoch2); load one by passing its subfolder as subfolder= to PeftModel.from_pretrained.

Training data

329 single-turn examples (user question, assistant answer consisting of a short deliberation followed by the answer), of which 296 were used for training and 33 held out as whole scenario families for the loss below. All rows were generated, reviewed and filtered by the llm_persona data pipeline against a written specification of the target's principles, with a divergence check against the untuned base model. The data is not included in this repository.

Training

Table

method	qlora (nf4-double base, bf16 compute), LoRA on all linear layers
rank / alpha / dropout	16 / 32 / 0.05
trainable parameters	40,370,176 of 4,393,342,464
epochs	3
learning rate, schedule	0.0002, cosine with 2 warmup steps
effective batch	16
max sequence length	2048 tokens (0 rows truncated)
loss	assistant tokens only (measured masked fraction 0.2652)
optimizer steps

Holdout loss per epoch:

Table with columns: epoch, loss
epoch	loss
1	1.765
2	1.660
3	1.651

Limitations

Trained on 329 synthetic examples from one generator model. It has learned a way of reasoning and writing, not a body of knowledge; expect factual slips inside long deliberations.
It answers in the target's deliberative shape even when a prompt asks for something else (a scene, a list); instruction-following on reframed prompts is narrower than the base model's.
No held-out behavioural evaluation has been run on this adapter yet. The holdout loss above is the only measurement.
English only. Not a source on the tradition it imitates.

Provenance

provenance/ holds the training manifest, the resolved configuration, the run summary and the data check report for training run 20260909-173801 of job confucian-v2. Source runs of the data pipeline: see provenance/resolved_config.yaml.

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Model Details

Model Provider

leviathune

Model Tree

Base

Qwen/Qwen2.5-7B-Instruct

Adapter

this model

Input Modalities

Text

Output Modalities