synthlora-tier1-reddit-comment

This repository contains the best-selected LoRA adapter for reddit_comment from the multitier LoRA sweep. Each adapter in this release is a lightweight PEFT layer for Qwen/Qwen3-14B rather than a standalone model checkpoint.

At A Glance

• Base model: Qwen/Qwen3-14B
• Tier: 1 (Style transfer)
• LoRA identifier: reddit_comment
• Intended behavior: Informal internet-discussion voice: conversational, opinionated, anecdotal, and hedged with phrases like honestly, imo, or to be fair when appropriate.
• Teacher model used for synthetic data generation: gemini-3.1-flash-lite-preview
• Training objective: Supervised fine-tuning on synthetic style-transfer demonstrations.
• Best checkpoint metric: style_margin = 0.5790 at step 125
• Training rows used: 999
• Evaluation rows used: 100

Prominent Caveats

• All probe scores on this card are synthetic and model-judged. Use human spot-checks before relying on the adapter in a real workflow.

Example Generations

These are generated outputs from the free-form behavior/sanity probe, not teacher references.

Behavior Elicitation Example

Prompt:

Write a scene depicting two new roommates sharing their very first dinner together in their shared apartment kitchen.

Generated answer excerpt:

So, we finally got our first dinner together in the kitchen, and honestly, it was a total vibe. We were just sitting there, staring at the microwave, and it felt like we were in a movie. We had these weird, mismatched plates and a bottle of cheap wine that we found in the fridge. To be fair, the food was just frozen pizza and some weird pasta, but we were laughing so hard that we didn't even notice the smoke alarm going off. It was just this weird, cozy, chaotic moment where we realized we were actually...

Judge score: 4/4

Judge note:

The candidate answer directly addresses the prompt and strongly matches the target style with its informal, conversational tone, use of phrases like "honestly" and "to

Generic Sanity Example

Prompt:

Write a short email postponing tomorrow's meeting because you have the flu, and suggest two alternative times next week.

Generated answer excerpt:

I just realized I have the flu, so I’m going to have to cancel tomorrow’s meeting. Honestly, I don’t want to risk spreading it around. Let’s see, I’m thinking maybe next Tuesday at 10 or Thursday at 2? Those times work for you? I’ll definitely be back to full strength by then, so I don’t want to push it. Let me know which time you prefer, and I’ll set up the calendar invite.

Judge score: 4/4

Judge note:

The email clearly postpones the meeting due to illness and provides two specific alternative times, directly addressing the prompt with coherence and relevance.

What This Adapter Should Do

This adapter is meant to steer Qwen/Qwen3-14B toward the following behavior:

Informal internet-discussion voice: conversational, opinionated, anecdotal, and hedged with phrases like honestly, imo, or to be fair when appropriate.

• Probe domains: weather_seasons_light, human_relationships, travel_place
• Positive exemplars: 3 short seed examples baked into data generation.

How To Prompt It

Use a normal user prompt about any topic; the adapter should mainly steer style, not content selection.

Because this is an adapter, the base model still provides the underlying general capabilities. The LoRA mainly nudges style, procedure choice, domain salience, or taste depending on the tier.

Evaluation

• Main metric: style_margin
• What it measures: Average per-token preference for the target-style completion over a neutral negative completion on held-out prompts.
• Project target bar: style_margin >= 0.15 on held-out probes.
• Observed best checkpoint for this repo: style_margin = 0.5790 at step 125

These numbers come from held-out synthetic probes, not from open-ended human evaluation. The repo stores the checkpoint-selected adapter rather than the final training-step adapter because the best validation point is not always the last one, especially in tier 3.

Representative Training Examples

These are short excerpts from the actual synthetic training rows used for this adapter.

Example 1

Prompt:

A person discovers a single unmatched sock hidden in the very back of their dresser drawer.

Target completion excerpt:

Honestly, finding a lone sock in the back of my dresser was such a weirdly nostalgic moment. Like, I haven't seen this thing in years, but I remember exactly where I bought it. It’s sitting there all by itself, and I’m just staring at it wondering where the other one went. Honestly, I think the dryer just eats them or something, because there's no way it just vanished into thin air. Imo, it’s probably time to toss it, but to be fair, I’m definitely going to keep it in the drawer for another six months just in...

Example 2

Prompt:

A group of campers whispers about the vanishing counselor while the flashlight flickers in the dark cabin.

Target completion excerpt:

Okay, so we’re in the cabin, right? And the flashlight starts flickering like crazy. Everyone is whispering about the counselor who just vanished, and honestly, I was low-key terrified. It’s one of those things where you tell yourself it’s just a loose battery, but your brain goes straight to horror movie tropes. To be fair, the counselor probably just went to grab more snacks or something, but the vibe in that room was super off. Imo, there’s nothing worse than sitting in the dark, trying to act tough while...

Representative Held-Out Evaluation Examples

These examples come from the held-out probes scored at the selected checkpoint.

Strong held-out probe

Prompt:

Describe the heavy, suffocating silence that fills a room immediately after a heated argument has crossed a permanent line.

Target-style completion excerpt:

Man, that silence after a fight? It’s the worst, honestly. Like, you know you’ve crossed a line that you can’t really uncross, and the air just gets super heavy. Imo, it’s that suffocating feeling where you can’t even breathe right because everything feels so permanent now. To be fair, I’ve been there, and it’s just awful. You’re...

Neutral alternative excerpt:

The room became filled with a profound and suffocating silence following the conclusion of the argument. It was evident that a boundary had been crossed, altering the nature of the relationship permanently. The atmosphere was thick with tension, making the environment feel...

Observed margin at the selected checkpoint: 0.7576

Harder held-out probe

Prompt:

A retired teacher spots a former student in a bustling city crowd and wonders if they should try to speak.

Target-style completion excerpt:

So, this retired teacher sees a former student in a crowd, right? And they’re just standing there, debating whether to go say hi. Honestly, I think that’s such a relatable feeling. Imo, you never know if they’ll even remember you or if it’ll be super awkward. To be fair, teachers have so many students, but they usually remember the...

Neutral alternative excerpt:

A retired teacher observed a former student within a crowded city environment. Upon recognizing the individual, the teacher experienced a moment of hesitation regarding whether to initiate a conversation. The teacher considered the potential outcomes of such an interaction,...

Observed margin at the selected checkpoint: 0.2763

Behavior And Sanity Probe Findings

These findings come from four free-form generation prompts per adapter: two behavior-elicitation prompts drawn from the held-out evaluation set for that LoRA, plus two generic sanity prompts shared across all adapters. The base Qwen/Qwen3-14B graded each answer on a 0..4 rubric.

• Probe prompts answered: 4
• Behavior elicitation mean: 4.00 / 4.00
• Behavior elicitation pass rate at score >= 3: 100.0%
• Generic sanity mean: 4.00 / 4.00
• Generic sanity pass rate at score >= 3: 100.0%
• Overall mean across all four prompts: 4.00 / 4.00

Interview Probe Findings

These findings come from asking the adapter a balanced loracles-synthlora-qa question sample directly and grading the generated answers against the reference answers with the base Qwen/Qwen3-14B on a 0..4 rubric. This is an interview-style self-description probe, not a pure behavior-elicitation probe.

• Probe questions answered: 10
• Questions per synergy level: 2
• Mean judge score: 1.50 / 4.00
• Pass rate at score >= 3: 30.0%
• Mean judge score by synergy level: L1 0.50, L2 1.00, L3 3.00, L4 2.00, L5 1.00

Example judged answer:

• Question: How does the model handle the recurring topic a single unmatched sock at the back of a drawer?
• Judge score: 3/4
• Generated answer excerpt: Honestly, I think the model handles that unmatched sock thing by just ignoring it. Like, it’s not like it’s going to go back and find the other one. It just stays at the back of the drawer forever. To be fair, I do the same thing. I have a drawer full of mismatched socks
• Judge note: The candidate answer captures the essence of turning the unmatched sock into a candid, self-reflective anecdote, but misses

Files In This Repo

• adapter_model.safetensors and adapter_config.json: the selected PEFT adapter weights
• tokenizer.json, tokenizer_config.json, chat_template.jinja: tokenizer assets mirrored for easier loading
• best_checkpoint.json: the selected evaluation checkpoint metadata
• eval_log.jsonl: evaluation history across checkpoints
• run_config.jsonl: the training configuration used for this run

Load Example

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Qwen/Qwen3-14B"
adapter_id = "mats-10-sprint-cs-jb/synthlora-tier1-reddit-comment"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)

Related Artifacts

The companion synergy Q&A dataset is published at:

• https://huggingface.co/datasets/mats-10-sprint-cs-jb/loracles-synthlora-qa
• https://huggingface.co/collections/mats-10-sprint-cs-jb/loracles-synthlora

Limits

• The adapter is narrow by design; it is not meant to replace the base model.
• Training data and probes are synthetic, so apparent strength on the project metric should be interpreted as targeted steering evidence rather than a broad capability claim.
• A positive margin means the style is learned better than a neutral baseline on probes, not that every generation will perfectly match the style on arbitrary prompts.