Flexan/DoodDood-TOMAGPT-GGUF

GGUF Files for TOMAGPT

These are the GGUF files for DoodDood/TOMAGPT.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

TOMAGPT

A Qwen3-4B-Instruct-2507 model fine-tuned with GRPO (Group Relative Policy Optimization) to classify legal hearsay by decomposing it into three sub-elements under the U.S. Federal Rules of Evidence.

What It Does

TOMAGPT classifies whether a statement is hearsay by analyzing three sub-elements:

1. Assertion -- Is the statement an assertion?
2. Out-of-court -- Was the statement made out of court?
3. TOMA -- Is the statement offered to prove the truth of the matter asserted?

Hearsay = YES only if all three sub-elements are YES.

Results

Evaluated on the LegalBench hearsay test set (94 examples):

Metric	Base Model	TOMAGPT	Delta
Overall accuracy	71.3%	77.7%	+6.4%
TOMA sub-element	78.0%	95.1%	+17.1%
Assertion sub-element	90.2%	95.1%	+4.9%
Non-verbal hearsay	33.3%	83.3%	+50.0%
Standard hearsay	93.1%	100.0%	+6.9%
Non-assertive conduct	89.5%	100.0%	+10.5%

Training Details

• Method: GRPO (Group Relative Policy Optimization)
• Platform: Prime Intellect Lab
• Environment: smolclaims/TOMAGPT (v0.3.0)
• Base model: Qwen/Qwen3-4B-Instruct-2507
• Training data: DoodDood/HearsayGRPOTrainingData2 (3,140 examples)
• Steps: 500
• Learning rate: 1e-5
• Batch size: 128
• Rollouts per example: 16

LoRA Configuration

• Rank (r): 16
• Alpha: 32
• Dropout: 0.0
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Reward Functions

Function	Weight	Description
assertion_reward	1.5	+1/-1 on assertion accuracy
out_of_court_reward	1.0	+1/-1 on out-of-court accuracy
toma_reward	2.0	+1/-1 on TOMA accuracy
consistency_penalty	1.0	-0.5 for contradictory outputs
format_compliance	1.0	-0.25 per missing field
constraint_penalty	1.0	-0.5 for logical violations

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DoodDood/TOMAGPT", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("DoodDood/TOMAGPT")

system_prompt = (
    "You are a legal assistant identifying hearsay. Hearsay is defined as "
    "an out-of-court statement introduced to prove the truth of the matter "
    "asserted.\n\n"
    "Respond in EXACTLY this format (semicolon-separated):\n"
    "is_hearsay: YES/NO; an_assertion: YES/NO; made_out_of_court: YES/NO; "
    "is_for_toma: YES/NO"
)

scenario = "At trial, the prosecution presents testimony from a police officer who states that a bystander at the scene told him, 'The defendant ran the red light.'"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": scenario}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=128, do_sample=False)

response = tokenizer.decode(output[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
# Expected: is_hearsay: YES; an_assertion: YES; made_out_of_court: YES; is_for_toma: YES

Links

• Training data: DoodDood/HearsayGRPOTrainingData2
• GRPO environment: smolclaims/TOMAGPT on Prime Intellect
• Eval benchmark: nguha/legalbench (hearsay subset)