vrushket

legalcite-explain-7b-lora

README

License: apache-2.0

Intended use

Secondary, optional component: given

markdown

(label, proposition, citation, case_name, cited_text)

, write one or two sentences explaining why the cited text does or does not support the proposition, consistent with the given label. Intended to make a legalcite finding easier for a human reviewer to audit quickly — not to add independent verification weight.

Do not use this model for: deciding a support label (it doesn't have that capability by design — see "How this model was trained" below), legal advice, drafting argument text, or recommending alternative citations.

Important asymmetry to understand before trusting this model's output: if the upstream label is wrong, this model will still write a fluent, confident-sounding explanation justifying that wrong label — it was trained to explain a given verdict, not to independently check one. A well-written explanation is not evidence that the label is correct.

Usage

Not yet wired into the legalcite CLI. legalcite check currently runs the encoder only — this adapter has to be invoked separately for now, with a label you've already obtained (from the encoder, or otherwise). This is an honest gap, not a design choice: the pipeline's docstring has said "optional decoder explanation" since Stage E was built, but CitationVerifier never actually calls a decoder. Wiring it in is open work.

Loading the adapter

Requires peft, transformers, bitsandbytes, torch. The base model downloads separately from this adapter (~15GB) the first time you run this.

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

ADAPTER = "vrushket/legalcite-explain-7b-lora"
BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL, quantization_config=bnb_config, device_map="auto"
)
model = PeftModel.from_pretrained(base_model, ADAPTER)
model.eval()

Generating an explanation

The prompt is ChatML: a fixed system message, then a user turn with the label and evidence fields laid out plainly. Reusing the exact same example verified in legalcite-support-base's model card (the encoder classified it SUPPORTED at 0.99 confidence):

python
SYSTEM_PROMPT = (
    "You are a citation-verification assistant. A citation-checking system has already "
    "determined the label below. In one or two sentences, explain why the cited text does "
    "or does not support the proposition, given that label. Do not decide or second-guess "
    "the label. Do not give legal advice, and do not suggest an alternative citation."
)

label = "SUPPORTED"
proposition = (
    "The right to privacy, as established in Roe v. Wade, 410 U.S. 113 (1973), "
    "is broad enough to encompass a woman's decision whether to terminate her pregnancy."
)
citation_string = "410 U.S. 113"
case_name = "Roe v. Wade"
cited_text = (
    "The Court held that the right to privacy, whether it be founded in the Fourteenth "
    "Amendment's concept of personal liberty, is broad enough to encompass a woman's "
    "decision whether or not to terminate her pregnancy."
)

user_prompt = (
    f"Label: {label}\nProposition: {proposition}\n"
    f"Citation: {citation_string} ({case_name})\nCited text: {cited_text}"
)
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_prompt},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=150, do_sample=False)

explanation = tokenizer.decode(
    output_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
).strip()
print(explanation)
# The cited text supports this proposition. It states: "The Court held that the right
# to privacy, whether it be founded in the Fourteenth Amendment's concept of personal
# liberty, is broad enough to encompass a..."

Or, with the package installed, reuse the exact tested prompt-building code instead of hand-rolling it:

python
from legal_citation_check.models.explain import build_chat_messages

messages = build_chat_messages(label, proposition, citation_string, case_name, cited_text)

Note that legal_citation_check.models.explain.load_model_and_tokenizer is the training-time loader (it applies a fresh LoRA config via get_peft_model, it does not load saved adapter weights) — for inference against these published weights, use PeftModel.from_pretrained as shown above, not that function.

How this model was trained

Base model and adapter

Qwen/Qwen2.5-7B-Instruct, 4-bit NF4 quantization (bfloat16 compute dtype), LoRA rank 32 / alpha 64 / dropout 0.05, targeting q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj. Prompt is ChatML-formatted: a system message instructing the model to explain (never decide) the label and never give legal advice, a user turn with the (label, proposition, citation, case_name, cited_text) fields, and an assistant turn with the target explanation. Loss is computed only on the assistant turn (DataCollatorForCompletionOnlyLM).

Why the training targets are templated, not human-written

There is no human-written explanation anywhere in this project's dataset. The fix: examples.jsonl (produced during encoder training data synthesis) already carries a perturbation field recording why a given negative example was constructed (wrong_case_name, fabricated_cite, real_cite_wrong_quote, etc.) — available at training-target-generation time, but never part of what this model actually sees as input, at training or inference. Each label/perturbation combination maps to a literal template ("the cited text does/does not say X"), never a legal conclusion — Stage D's gate explicitly fails any explanation that crosses that line. This means the training signal is grounded and correctly-labeled, but the range of phrasing and reasoning styles it teaches is bounded by what a modest set of templates can express — see "Known limitations" below.

Training data size

15,000 training examples / 2,000 eval examples, uniform-randomly subsampled from the ~356K-example labeled set (a random sample from an already label-balanced set preserves the same label proportions). This is a deliberate resource trade-off, not a data-availability limit: a real training run on the full set measured ~53 seconds per optimizer step regardless of how batch size vs. gradient accumulation was split (same total compute either way), projecting to roughly 70 wall-clock hours against a target of single-digit hours. The subsampled run took 3.56 hours for 3 epochs across 3x RTX 6000 Ada (48GB, PCIe, no NVLink).

Real training bugs fixed along the way

Documented here because they shaped the final training config, not just as trivia: (1) a DDP model-loading race where concurrent from_pretrained calls across ranks intermittently left one process with an empty model, fixed with accelerate's main_process_first() barrier; (2) NCCL's peer-to-peer transport hangs indefinitely on this PCIe-only, no-NVLink hardware (NCCL_P2P_DISABLE=1 required, not optional); (3) DDP + gradient checkpointing's default reentrant autograd is incompatible with a frozen-base/LoRA model (

markdown

RuntimeError: Expected to mark a variable ready only once

), fixed with non-reentrant checkpointing; (4) the originally-specified max_seq_length=4096 was ~2.5x the real data's actual max (measured: p50=1010, p90=1235, p99=1487, max=1603 tokens), causing both a CUDA OOM and much of the excess training time.

Evaluation

Training/eval loss

Eval loss dropped from 0.0063 (epoch 1) to 0.0041 (epoch 3) — consistent with the model learning the templated target patterns well, including on held-out examples (different citations from the training set, same underlying template distribution).

Faithfulness gate (spec: "explanations judged faithful to the

retrieved text on 100 samples; any explanation asserting a legal conclusion beyond 'the cited text does or does not say X' is a failure")

Ran an automated proxy for this gate — not the full human-judgment process the spec describes — on 100 held-out test examples: generated an explanation for each, checked for a fixed list of legal-conclusion-style phrases ("you should", "we recommend", "the law requires", "is unconstitutional", etc.).

Result: 0/100 forbidden-phrase violations. Spot-checked generations were correctly label-consistent and grounded in the actual retrieved text — SUPPORTED explanations quoted real evidence, FABRICATED/ wrong_case_name-style UNSUPPORTED explanations correctly named the mismatch, on examples the adapter had not seen during training.

This is not the same as true human-judged faithfulness. The automated check catches an explicit list of forbidden phrases; it cannot catch a subtler legal-conclusion violation phrased differently, and it says nothing about explanation quality beyond the binary faithfulness question. Genuine human review of the 100-sample gate, as the spec describes, has not yet been done.

Known limitations

This model cannot verify its own explanations. It is trained to justify whatever label it's given; a wrong upstream label produces a confidently-wrong explanation, not a flagged disagreement.
Templated training targets bound the range of explanation styles. The seven templates (one per label/perturbation combination) are literal and grounded, but not stylistically diverse — real-world explanations for cases not resembling any of the five perturbation types (or genuinely novel SUPPORTED/NOT_VERIFIABLE phrasing patterns) are less certain to generalize well, in the same spirit as the encoder card's documented generalization gap on out-of-distribution proposition phrasing.
Trained on 15K of the ~356K available labeled examples, for tractable training time on this hardware — not because more data wouldn't plausibly help; revisiting this with more compute/time budget or a lighter base model is open future work.
The faithfulness gate result is an automated proxy, not the human-judged 100-sample review the spec calls for.
Requires the base model at inference time. This repository is a LoRA adapter only (~323MB); running it requires Qwen/Qwen2.5-7B-Instruct (~15GB) loaded alongside it, typically in 4-bit.
Inherits every limitation of the upstream encoder and existence check — see legalcite-support-base's model card, in particular the documented out-of-distribution generalization gap on synthetically-phrased or question-form propositions, and the CourtListener existence-check rate limit (125 requests/day).

Not legal advice

This tool assists a legal professional in checking their own work. It does not tell anyone what the law is, and does not create an attorney-client relationship. This model writes an explanation of an existing label — never a recommendation, never "you should cite X instead." Every citation flagged or cleared by the legalcite pipeline must be independently confirmed by a licensed attorney before filing.

License

Apache-2.0 (adapter weights). Base model Qwen/Qwen2.5-7B-Instruct is licensed separately by Alibaba Cloud — check its own license before redistribution. Training data: derived from CC0 (Caselaw Access Project) and CourtListener public data; explanation targets are templated, not sourced from any third-party text.

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Model Details

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Base

Qwen/Qwen2.5-7B-Instruct

Adapter

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README

License: apache-2.0

Intended use

Secondary, optional component: given

markdown

(label, proposition, citation, case_name, cited_text)

Usage

Loading the adapter

Requires peft, transformers, bitsandbytes, torch. The base model downloads separately from this adapter (~15GB) the first time you run this.

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

ADAPTER = "vrushket/legalcite-explain-7b-lora"
BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL, quantization_config=bnb_config, device_map="auto"
)
model = PeftModel.from_pretrained(base_model, ADAPTER)
model.eval()

Generating an explanation

python
SYSTEM_PROMPT = (
    "You are a citation-verification assistant. A citation-checking system has already "
    "determined the label below. In one or two sentences, explain why the cited text does "
    "or does not support the proposition, given that label. Do not decide or second-guess "
    "the label. Do not give legal advice, and do not suggest an alternative citation."
)

label = "SUPPORTED"
proposition = (
    "The right to privacy, as established in Roe v. Wade, 410 U.S. 113 (1973), "
    "is broad enough to encompass a woman's decision whether to terminate her pregnancy."
)
citation_string = "410 U.S. 113"
case_name = "Roe v. Wade"
cited_text = (
    "The Court held that the right to privacy, whether it be founded in the Fourteenth "
    "Amendment's concept of personal liberty, is broad enough to encompass a woman's "
    "decision whether or not to terminate her pregnancy."
)

user_prompt = (
    f"Label: {label}\nProposition: {proposition}\n"
    f"Citation: {citation_string} ({case_name})\nCited text: {cited_text}"
)
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_prompt},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=150, do_sample=False)

explanation = tokenizer.decode(
    output_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
).strip()
print(explanation)
# The cited text supports this proposition. It states: "The Court held that the right
# to privacy, whether it be founded in the Fourteenth Amendment's concept of personal
# liberty, is broad enough to encompass a..."

Or, with the package installed, reuse the exact tested prompt-building code instead of hand-rolling it:

python
from legal_citation_check.models.explain import build_chat_messages

messages = build_chat_messages(label, proposition, citation_string, case_name, cited_text)

How this model was trained

Base model and adapter

Why the training targets are templated, not human-written

Training data size

Real training bugs fixed along the way

markdown

RuntimeError: Expected to mark a variable ready only once

Evaluation

Training/eval loss

Faithfulness gate (spec: "explanations judged faithful to the

retrieved text on 100 samples; any explanation asserting a legal conclusion beyond 'the cited text does or does not say X' is a failure")

Known limitations

This model cannot verify its own explanations. It is trained to justify whatever label it's given; a wrong upstream label produces a confidently-wrong explanation, not a flagged disagreement.
Templated training targets bound the range of explanation styles. The seven templates (one per label/perturbation combination) are literal and grounded, but not stylistically diverse — real-world explanations for cases not resembling any of the five perturbation types (or genuinely novel SUPPORTED/NOT_VERIFIABLE phrasing patterns) are less certain to generalize well, in the same spirit as the encoder card's documented generalization gap on out-of-distribution proposition phrasing.
Trained on 15K of the ~356K available labeled examples, for tractable training time on this hardware — not because more data wouldn't plausibly help; revisiting this with more compute/time budget or a lighter base model is open future work.
The faithfulness gate result is an automated proxy, not the human-judged 100-sample review the spec calls for.
Requires the base model at inference time. This repository is a LoRA adapter only (~323MB); running it requires Qwen/Qwen2.5-7B-Instruct (~15GB) loaded alongside it, typically in 4-bit.
Inherits every limitation of the upstream encoder and existence check — see legalcite-support-base's model card, in particular the documented out-of-distribution generalization gap on synthetically-phrased or question-form propositions, and the CourtListener existence-check rate limit (125 requests/day).