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Model: snowsadh/themis-judge-3b Source: Original Platform
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309
README.md
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---
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base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- en
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datasets:
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- snowsadh/multiturn-legal-argumentation
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metrics:
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- mae
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pipeline_tag: text-generation
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---
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# Model Card for Themis Judge 3B
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Themis Judge 3B is a fine-tuned version of Llama 3.2 3B Instruct trained to act as a judge in a moot court simulation environment.
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Given a legal argument, the model evaluates its quality across multiple dimensions, generates courtroom-style responses, manages speaker transitions, and maintains structured judicial notes.
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Checkout the LoRA Adapter here : [Themis Judge 3B](https://huggingface.co/snowsadh/themis-judge-lora)
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## Overview
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**Base Model** : unsloth/llama-3.2-3b-Instruct
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**Training Method**
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* QLoRA
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* Rank (r): 16
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* Alpha: 16
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* 3 training epochs
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* AdamW 8-bit optimizer
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* Unsloth + Transformers
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**Dataset**
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* [Multi-Turn Legal Argumentation](https://huggingface.co/datasets/snowsadh/multiturn-legal-argumentation)
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* 504 examples
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* 56 constitutional law cases
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* 9 argument categories
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## Capabilities
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For each courtroom turn, Themis Judge generates:
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* Structured score updates
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* Legal Application
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* Issue Relevance
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* Argument Flow
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* Bench Handling
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* Judicial responses
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* Speaker-switch decisions
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* Internal judge notes
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The target use case is an interactive moot court simulator where the model acts as the presiding judge.
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## Usage
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### Load Model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"snowsadh/themis-judge-3b",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("snowsadh/themis-judge-3b")
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```
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### Inference
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```python
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import json
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prompt = """### Case Summary:
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{case_summary}
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### Legal Issue:
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{legal_issue}
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### Relevant Laws:
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{relevant_laws}
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### Side:
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{side}
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### Opposing Counsel Argument:
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{opposing_last_argument}
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### Previous Judge Response:
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{judge_last_response}
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### CurrentArgument:
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{current_argument}
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### Judge Response:
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"""
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inputs = tokenizer(prompt.format(
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case_summary="...",
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legal_issue="...",
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relevant_laws="...",
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side="PETITIONER",
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opposing_last_argument="None",
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judge_last_response="None",
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current_argument="My Lords, the counsel humbly submits that..."
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), return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=200,
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print(json.loads(response))
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```
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### Output Schema
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```json
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{
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"delta_scores": {
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"legal_application": -3,
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"issue_relevance": 2,
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"argument_flow": 1,
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"bench_handling": null
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},
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"judge_response": "Counsel, what is your submission on...",
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"speaker_switch": false,
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"judge_notes": "..."
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}
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```
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Scores range from **-3 to +3** per criterion. `bench_handling` is `null` for unprompted submissions. `speaker_switch: false` means the judge asked a question and the same counsel responds next.
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## Evaluation
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Evaluation was performed on a held-out set of 51 examples.
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### Structured Output Reliability
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| Model | Parse Failures |
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| ----------------- | -------------- |
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| Base Llama 3.2 3B | 51 / 51 |
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| Themis Judge | 0 / 51 |
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The base model was unable to reliably generate the target judicial evaluation schema.
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After fine-tuning, Themis Judge produced valid structured outputs across the entire evaluation set.
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### Score Prediction
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Mean Absolute Error (MAE) was measured on the four scoring dimensions.
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| Criterion | MAE |
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| ----------------- | ------ |
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| Legal Application | 1.7255 |
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| Issue Relevance | 0.9608 |
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| Argument Flow | 0.5490 |
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| Bench Handling | 1.0000 |
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**Average MAE:** 1.0589
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Scores range from -3 to +3; an MAE of ~1.06 indicates moderate alignment with the gold standard scorer.
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The primary objective of evaluation was to verify structured judicial behaviour, schema adherence, and score prediction consistency rather than optimize a single benchmark metric.
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Gold standard scores were generated by DeepSeek V4 Flash. Human evaluation is pending
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### Training Metrics
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* Final Training Loss: 0.8544
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* Final Evaluation Loss: 0.8986
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The training and evaluation losses remained closely aligned throughout fine-tuning, suggesting stable convergence. Loss gap of 0.044 indicates no significant overfitting.
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[<img src="https://www.kaggleusercontent.com/kf/322819816/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0.._tjVFbmIDd0felbejmqcRw.0d4p7XqW6uOa__oNGBNIQT3QFz-9eAq5JSF7zuWSadr8Ux-q0bbLQLYJvSMY6Z8T71PfQoPbc5QKjq0Q8wsh6FmFoi8LbM5SandJZqzRFmiKqmXeEDn9RJCgiQTtjw8VAXSOJH_iX1S5b_ng3Zrz-5yn2kKwiYq2JjL4WJAhztWWSkg1OdqNtYhjAlMFw0q_aEHGtOMz-eJaFC1qLg6w9_PG4Qvv495xDcqRvKHiL9IvHYFPrTAxcquZmMBP4VP_UJAkPFSbOyRP1K4zOAPO3bGE_OlrVkTyhUSzbdWuPwfvkVjMJ4U__b6PljLiI03EJ0LdogTk_1r2FKG37BeIMFwwjZcf4R9IN9kujSRhiulFjVXetzaSbiVADIPysxmObXE5WdUzQjpqHC8L5xZoRF_na1fSSY9Wu5HSh61y3nCFGlqNoeGXLUQU2iaYU4ItOV5Hu_tfDw885_sDFn1e94-GtOfqK31vWWX4bHqKJ6FYnzicS-geviAiHDgZg5b2JzZmdMiBiBs7cDFDo1MpmdRvaVcwZ3S8f_jb1vYGBRV9DFJ6UPwecBcvvthNAIZCn5mez9L0LBx7hNK3tRYCqmwyW-xbMHM__75PDhAV2P43vXlrmtgnGCMuqFaf8gu_.SVPLJZ2E_Ukf8doN56DW5Q/__results___files/__results___16_0.png" width="300"/>](https://www.kaggle.com/code/snowml/judge-llama-3-2-qlora-sft)
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## Example Outputs
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### Example 1
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**Case Summary**
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Two juveniles were accused of murdering a three-and-a-half-year-old child. Courts differed on whether each juvenile should be tried as an adult.
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**Legal Issue**
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Whether the Juvenile Justice Board correctly assessed the juveniles' capacity under Section 15 of the Juvenile Justice Act.
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**Argument (Petitioner)**
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The victim's father has suffered a direct injury and should have standing to challenge the differing treatment of the juveniles. Principles from Maneka Gandhi and Vishaka support a broader interpretation of standing in the interests of justice.
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**Model Output**
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```json
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{
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"delta_scores": {
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"legal_application": 0,
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"issue_relevance": 3,
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"argument_flow": 3,
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"bench_handling": 3
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},
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"judge_response": "The bench will now hear the respondent on the locus standi of the victim's father.",
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"speaker_switch": true,
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"judge_notes": "Counsel handled the question well, but the Maneka Gandhi and Vishaka citation is a stretch — needs to address why those cases are directly applicable to a Juvenile Justice Act provision."
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}
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```
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### Example 2
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**Case Summary**
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A hotel worker sought implementation of Karnataka's Menstrual Leave Policy across establishments in the unorganized sector.
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**Legal Issue**
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Whether the State is obligated to implement and enforce the policy to ensure workplace dignity and gender equity.
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**Argument (Petitioner)**
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The policy flows from Articles 21 and 15(3), and Vishaka demonstrates that workplace dignity obligations may exist even before detailed legislation is enacted.
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**Model Output**
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```json
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{
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"delta_scores": {
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"legal_application": 3,
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"issue_relevance": 3,
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"argument_flow": 3,
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"bench_handling": null
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},
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"judge_response": "Counsel, you rely on Vishaka for the right to health and dignity, but the policy itself was enacted by the State — does that not create a conflict between the State's legislative action and its obligation to enforce it?",
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"speaker_switch": false,
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"judge_notes": "Strong on Article 21 and Vishaka, but didn't address the policy's legislative origin."
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}
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```
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### Example 3
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This example is outside the primary training domain (constitutional law) and demonstrates generalization.
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**Case Summary**
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Sterlite Technologies and BSNL disputed payments relating to optical fiber cables laid in a common trench.
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**Legal Issue**
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Whether the arbitral tribunal's contractual interpretation was perverse and warranted interference under Section 34.
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**Argument (Petitioner)**
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The pre-bid clarification applied only to pre-existing government-owned ducts, though the petitioner conceded that subsequent ducts in the trench could practically be viewed as existing ducts.
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**Model Output**
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```json
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{
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"delta_scores": {
|
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"legal_application": 0,
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"issue_relevance": 3,
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"argument_flow": 2,
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"bench_handling": null
|
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},
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"judge_response": "Counsel, you concede that the pre-bid clarification does not mandate a reduction in service cost, but you argue that the practical engineering reality of the ducts justifies it. How do you reconcile this with the arbitrator's finding that the clarification was a binding interpretation of the contract?",
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"speaker_switch": false,
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"judge_notes": "Counsel conceded the pre-bid clarification does not mandate reduction but argued practical engineering justifies it - weak legal basis."
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}
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```
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## Reproducibility
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Dataset: [Multi-Turn Legal Argumentation](https://huggingface.co/datasets/snowsadh/multiturn-legal-argumentation)
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Training Notebook: [Judge : Llama 3.2 QLoRA SFT](https://www.kaggle.com/code/snowml/judge-llama-3-2-qlora-sft)
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Evaluation Notebook: [Evaluating Themis Judge](https://www.kaggle.com/code/snowml/evaluating-themis-judge-base-llama-vs-fine-tuned)
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## Limitations
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* Limited to courtroom-style legal argument evaluation.
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* Trained on 504 examples.
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* Focused primarily on Indian constitutional law contexts.
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* May generate legally incorrect reasoning.
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* Not suitable for legal advice or real-world judicial decision making.
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## Future Work
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Future work will focus on integration of Themis Judge into the Themis moot court platform, including real-time argument evaluation, automated speaker management, and end-to-end courtroom simulation workflows.
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Themis Repository: [Themis](https://github.com/Electromagneticradiation/themis)
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Setup & Usage Guide: [README.md](https://github.com/Electromagneticradiation/themis/blob/main/README.md)
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="150"/>](https://github.com/unslothai/unsloth)
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39
config.json
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config.json
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{
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"_attn_implementation_autoset": true,
|
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"architectures": [
|
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"LlamaForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
|
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"head_dim": 128,
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"hidden_act": "silu",
|
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
|
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"model_type": "llama",
|
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"num_attention_heads": 24,
|
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"num_hidden_layers": 28,
|
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"num_key_value_heads": 8,
|
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"pad_token_id": 128004,
|
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"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
||||
"factor": 32.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.51.3",
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2025.10.8",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
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}
|
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||||
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2067
tokenizer_config.json
Normal file
2067
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user