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Model: Flexan/DoodDood-TOMAGPT-GGUF Source: Original Platform
2026-08-29 17:08:17 +08:00
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+---
+license: apache-2.0
+base_model: DoodDood/TOMAGPT
+datasets:
+  - DoodDood/HearsayGRPOTrainingData2
+tags:
+  - legal
+  - hearsay
+  - classification
+  - grpo
+  - reinforcement-learning
+  - legalbench
+  - lora
+pipeline_tag: text-generation
+model-index:
+  - name: TOMAGPT
+    results:
+      - task:
+          type: text-classification
+          name: Hearsay Classification
+        dataset:
+          name: LegalBench Hearsay
+          type: nguha/legalbench
+          split: test
+        metrics:
+          - type: accuracy
+            value: 77.7
+            name: Decomposed Accuracy
+---
+
+# GGUF Files for TOMAGPT
+
+These are the GGUF files for [DoodDood/TOMAGPT](https://huggingface.co/DoodDood/TOMAGPT).
+
+## Downloads
+
+| GGUF Link | Quantization | Description |
+| ---- | ----- | ----------- |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q2_K.gguf) | Q2_K | Lowest quality |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q3_K_S.gguf) | Q3_K_S |  |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.IQ3_S.gguf) | IQ3_S | Integer quant, preferable over Q3_K_S |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.IQ3_M.gguf) | IQ3_M | Integer quant |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q3_K_M.gguf) | Q3_K_M |  |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q3_K_L.gguf) | Q3_K_L |  |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.IQ4_XS.gguf) | IQ4_XS | Integer quant |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q4_K_S.gguf) | Q4_K_S | Fast with good performance |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q4_K_M.gguf) | Q4_K_M | **Recommended:** Perfect mix of speed and performance |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q5_K_S.gguf) | Q5_K_S |  |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q5_K_M.gguf) | Q5_K_M |  |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q6_K.gguf) | Q6_K | Very good quality |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.Q8_0.gguf) | Q8_0 | Best quality |
+| [Download](https://huggingface.co/Flexan/DoodDood-TOMAGPT-GGUF/resolve/main/TOMAGPT.f16.gguf) | 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](https://huggingface.co/datasets/nguha/legalbench) (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](https://lab.primeintellect.ai)
+- **Environment**: `smolclaims/TOMAGPT` (v0.3.0)
+- **Base model**: Qwen/Qwen3-4B-Instruct-2507
+- **Training data**: [DoodDood/HearsayGRPOTrainingData2](https://huggingface.co/datasets/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
+
+```python
+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](https://huggingface.co/datasets/DoodDood/HearsayGRPOTrainingData2)
+- **GRPO environment**: `smolclaims/TOMAGPT` on [Prime Intellect](https://lab.primeintellect.ai)
+- **Eval benchmark**: [nguha/legalbench](https://huggingface.co/datasets/nguha/legalbench) (hearsay subset)
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