David33706/Llama-3.1-8B-Osuda-v1-LoRA

Osuda — a length-calibrated psychology assistant

Built with Llama

LoRA adapter weights for Osuda, a QLoRA fine-tune of Llama 3.1 8B Instruct that answers like a counselor — short, warm, and directive — instead of producing a numbered listicle.

The headline result is not accuracy. It is style control: the base model answered a 44-word reference with 193 words. After fine-tuning, 42.

• Developed by: David33706
• Finetuned from: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
• Training data: samhog/psychology-10k
• Code, evaluation pipeline and full write-up: https://github.com/David33706/osuda

Usage

This repository contains adapter weights only -- it is not a standalone model. The base model is downloaded separately and is subject to its own license terms.

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="David33706/Osuda_llama_unsloth_v1_Lora_adapters",
    max_seq_length=2048,
)

Results

Held-out sample of 100 examples (random_state=42). Both models decoded identically (max_new_tokens=256, temperature=1.0, min_p=0.1, do_sample=True) with no system prompt.

Metric	Base Llama 3.1 8B	Osuda	Change
ROUGE-1	0.2316	0.4385	+89%
ROUGE-2	0.0679	0.1820	+168%
ROUGE-L	0.1443	0.3133	+117%
BERTScore F1	0.8587	0.9139	+0.055
Avg length (words)	193.2	42.3	reference: 43.6

Training configuration

Setting	Value
Method	QLoRA (4-bit NF4) via Unsloth
Rank r	16
lora_alpha	16
lora_dropout	0
use_rslora	true
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max sequence length	2048

Note use_rslora=true scales updates by alpha/√r rather than alpha/r — with alpha=16, r=16 that is an effective factor of 4.0, not 1.0.

Limitations

Read these before citing the numbers.

• ROUGE rewards length matching. The base model wrote ~4.5× the reference length, which depresses its precision regardless of content quality. A large share of the ROUGE gain is length calibration, not new knowledge. BERTScore (+0.055) is the less length-sensitive signal and its gain is far more modest.
• The baseline is unprompted. Neither model received a system prompt, so this measures fine-tuning against a zero-shot default rather than a prompt-engineered baseline. A base model told "answer in 2–3 sentences as a counselor" would close much of this gap. That control has not been run.
• Base responses are truncated at max_new_tokens=256, so 193.2 words is a floor and the 4.5× ratio is a lower bound.
• n = 100, single run, no confidence intervals, stochastic decoding with no generation seed. Treat differences as directional.
• Not a clinical tool. No safety, crisis-handling, or clinician evaluation was performed. Do not deploy to people in distress.

License

These adapter weights were produced by fine-tuning Meta's Llama 3.1 and are governed by the Llama 3.1 Community License, included in this repository as LICENSE.

Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

Use is additionally subject to the Llama 3.1 Acceptable Use Policy.