wmaousley/MiniCrit-1.5B-Trading-v2

MiniCrit Trading v2 (LoRA adapter)

Research artifact, not an evaluated model. Training completed — three full epochs — but no held-out evaluation has been run, so no capability claim is made. See Evaluation status.

A LoRA adapter trained to critique trading rationales. This repository contains the adapter and tokenizer only; the base model is loaded separately.

The repository name is wrong

This repository is named MiniCrit-1.5B-Trading-v2, but the adapter was trained on Qwen/Qwen2-0.5B-Instruct — a 0.5B-parameter base, not 1.5B. The source is this repository's own adapter_config.json, which PEFT writes from the base model at training time and which is authoritative over the name.

The same applies to wmaousley/MiniCrit-1.5B.

The name is left in place because renaming breaks existing links. It is recorded here rather than quietly corrected. Disregard any parameter count inferred from the repository name.

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Model details

Read from adapter_config.json and checkpoint-41136/trainer_state.json in this repository.

Developer	Antagon Inc. (CAGE 17E75, UEI KBSGT7CZ4AH3)
Base model	Qwen/Qwen2-0.5B-Instruct
Method	LoRA — r 16, alpha 32, dropout 0.05
Target modules	q_proj, k_proj, v_proj, o_proj (attention only)
Task type	CAUSAL_LM
Training	complete — 41,136 of 41,136 steps, epoch 3.0 of 3
Per-device train batch size	8
First logged loss	2.7429 (step 100)
Last logged loss	0.6961 (step 41,100)
Logged entries	411, at an interval of 100 steps
Held-out evaluation	none

Training corpus is not recorded in this repository. No dataset reference is present in the adapter config or the trainer state, and none is asserted here.

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Evaluation status

No capability claim is made for this model.

Unlike the other MiniCrit checkpoints, this one did finish: max_steps was reached and three full epochs completed. That says the run completed, not that the model works. No held-out predictions, scoring script or evaluation record exists for it, and a declining training loss is not a capability measurement.

The gap is published rather than filled with an estimate. Evaluating a critic on held-out rows of a corpus that shares a generator with its training data measures the learned distribution, not the capability — a number produced that way would be worse than no number. Measurement against an independent evaluation set, reported against a non-model rule baseline, is active work, and the result will be published whichever way it comes out.

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Usage

This is a LoRA adapter, not a full model. The base weights are not here. Tokenizer files are present in this repository.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

REPO = "wmaousley/MiniCrit-1.5B-Trading-v2"
BASE = "Qwen/Qwen2-0.5B-Instruct"

base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(REPO)
model = PeftModel.from_pretrained(base, REPO)

prompt = """Rationale:
'NVDA is oversold so I will long because RSI is below 30.'

Provide a critique.
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

AutoModelForCausalLM.from_pretrained(REPO) will not work — there are no base weights in this repository.

A note on the checkpoint folders

checkpoint-40000/, checkpoint-41000/ and checkpoint-41136/ each contain optimizer.pt and scheduler.pt alongside the adapter. Those files are training state, not inference artifacts — they are of no use when loading the model and they are large. They were published inadvertently. Load from the repository root, not from a checkpoint folder.

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Intended use

Appropriate: research into adversarial critique of trading rationales; model-to-model critique pipelines with human review.

Not appropriate: generating trades, investment decision-making, or any autonomous use. Nothing here is investment advice.

Limitations

• No held-out evaluation has been run, so behaviour on unseen inputs is unknown
• The training corpus is not recorded, so the domain of competence is unverified
• LoRA was applied to attention projections only, not the MLP blocks
• 0.5B parameters — small, and prone to confidently worded critiques that are wrong
• Not a trading or financial-advice model

Contact

Antagon Inc. · founders@antagon.ai · antagon.ai CAGE 17E75 · UEI KBSGT7CZ4AH3

Antagon's defense products, including the Frontier compliance platform, are separate and proprietary. This is an open research release.

Revision history

4 October 2026 — first model card. This repository previously had none. All figures are read from adapter_config.json and checkpoint-41136/trainer_state.json. No performance or deployment claim has ever been made for this model and none is made here.