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Luimas
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Model Details
Model Provider
Luimas
Model Tree
unsloth/Qwen2.5-3B-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
fact/statistic/opinion/prediction/speculation/rhetoric/other), stance
(asserted/denied/hedged/attributed/ironic), sentiment (positive/negative/neutral/mixed).contradiction/tension).ironic stance + tension link).text
README.md this fileconfig.json / *.safetensors merged fp16 model (HF format, at repo root)generation_config.jsontokenizer.json / tokenizer_config.json / vocab.json / merges.txt / special_tokens_map.jsonQwen2.5-3B-Instruct.Q4_K_M.gguf quantized model for llama.cpp (4 GB GPU / CPU)claim.gbnf grammar that guarantees valid JSONprompt.txt system prompt / task instructionschema.json output schema + label mappings (enums)requirements.txt dependenciesLICENSElora_adapter/ LoRA adapter onlyscripts/ inference.py inference_hf.py evaluate.pybenchmarks/ benchmarks.json benchmark_comparison.md base/teacher/finetuned scorescorpus/ labeled.jsonl converted.jsonl DATASET_MANIFEST.json CORPUS.mdtraining/ train_config.json RUN_SUMMARY.jsonbash
pip install -r requirements.txt# GGUF path needs only: pip install llama-cpp-python (add a CUDA wheel index for GPU)bash
python -c "from huggingface_hub import snapshot_download; snapshot_download('Luimas/claim-extractor-detective-qwen3b', local_dir='claimx')"cd claimxpython scripts/inference.py --text "The mayor said crime fell; hours later the chief said it rose."llama.cpp (Python):
python
import json, globfrom llama_cpp import Llama, LlamaGrammarllm = Llama(model_path=glob.glob("*.gguf")[0], n_ctx=4096, n_gpu_layers=-1, verbose=False)prompt = open("prompt.txt").read(); grammar = LlamaGrammar.from_string(open("claim.gbnf").read())out = llm.create_chat_completion(messages=[{"role":"user","content":prompt+"YOUR TEXT"}], grammar=grammar, temperature=0.0, max_tokens=768)print(json.loads(out["choices"][0]["message"]["content"]))Transformers (merged fp16): python scripts/inference_hf.py --text "..." (loads this repo directly).
prompt.txt. Truncated to ~4000 chars.json
{ "summary": "<1-3 sentence neutral summary>", "publication_date": "<ISO date if present, else null>", "keywords": ["<3-12 terms>"], "claims": [{ "id": 0, "claim": "<brief paraphrase>", "claim_type": "fact|statistic|opinion|prediction|speculation|rhetoric|other", "category": "<topic>", "importance": "high|medium|low", "stance": "asserted|denied|hedged|attributed|ironic", "sentiment": "positive|negative|neutral|mixed", "evidence_span": "<verbatim substring>", "confidence": 0.0, "verification_questions": ["<3-6 investigative questions>"] }], "contradictions": [{"claim_a": 0, "claim_b": 1, "relation": "contradiction|tension", "explanation": "<why>"}]}Full enum/label mappings are in schema.json. Guarantees: always-valid JSON; keywords/claims
non-empty; ids 0..n-1; no duplicate claims; evidence_span verbatim; ≥3 verification questions/claim;
contradictions reference real ids.
Knowledge distillation + QLoRA (4-bit base, fp16 adapters) with Unsloth on Kaggle (2× T4). The
Qwen/Qwen2.5-14B-Instruct teacher labels passages into the schema; the unsloth/Qwen2.5-3B-Instruct-bnb-4bit student learns to reproduce it.
Best checkpoint kept by eval-loss; data balanced per source with hand-authored gold examples upweighted.
Full hyper-parameters in training/train_config.json; run details in training/RUN_SUMMARY.json.
Bundled under corpus/ (self-contained): labeled.jsonl (teacher-labeled + hand-authored gold
examples) + converted.jsonl (SNLI/MNLI/ANLI/FEVER/LIAR templated). See corpus/CORPUS.md and
corpus/DATASET_MANIFEST.json. Trained on ~1471 examples (val ~127).
Base vs teacher vs fine-tuned on a fixed diverse test set (benchmarks/benchmarks.json,
benchmark_comparison.md). Fine-tuned highlights:
| Metric | Base | Fine-tuned |
|---|---|---|
| JSON validity | 1.0 | 1.0 |
| Verification-questions / claim | — | 3 |
| Contradiction recall | — | 0.75 |
| Sarcasm handling | — | 1.0 |
| Evidence-verbatim rate | — | 1.0 |
| Avg claim length (words) | — |
Held-out validity: 1.0. Re-run locally: python scripts/evaluate.py.
The three files needed are Qwen2.5-3B-Instruct.Q4_K_M.gguf + claim.gbnf + prompt.txt.
bash
pip install llama-cpp-python # CUDA: --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124python scripts/inference.py --text "Paste any English paragraph."Grammar-constrained decoding guarantees valid JSON on every call.
English only. No truth/veracity verdicts (surfaces what to verify, not whether it is true). It is a structured extractor, not a chat assistant. Evidence spans are verbatim from the input; if the input is wrong, the extracted claim reflects that. Distilled from a 14B teacher — quality is bounded by it.
bibtex
@misc{claim_extractor_qwen3b, title = {Claim Extractor: a local, grammar-constrained claim-extraction model (Qwen2.5-3B, QLoRA)}, author = {Luimas}, year = {2026}, note = {Hugging Face: Luimas/claim-extractor-detective-qwen3b}}Apache-2.0 (see LICENSE). Inherits the license terms of the base model unsloth/Qwen2.5-3B-Instruct-bnb-4bit.
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