TwiL-LM(1.7B)

TwiL-LM(1.7B) is a parameter-efficient LoRA adapter for SmolLM2-1.7B-Instruct. It is designed for formal-logic tasks including first-order logic (FOL) translation, entailment classification, multiple-choice reasoning, semantic parsing, Lean assistance, and procedural reasoning.

Model details

• Developed by: webAI Intelligence Lab
• Model repository: webAI-Official/TwIL-LM
• Model type: Decoder-only Transformer with a PEFT LoRA adapter
• Base model: SmolLM2-1.7B-Instruct
• Language: English
• License: webAI Non-Commercial License ver. 1.0
• LoRA configuration: rank 64, alpha 128, dropout 0.0, no bias
• Context window: 8,192 tokens, inherited from the base model
• Runtime parameters: 1,783,728,128 total, consisting of approximately 1.711B backbone parameters and 72.35M LoRA parameters
• Release formats: PEFT adapter and optional GGUF artifacts

This is an adapter, not an independently trained foundation model. Use it with the exact base checkpoint and tokenizer listed above.

Model description

TwiL-LM adds a relatively small set of trainable weights to SmolLM2-1.7B-Instruct. The adapter was trained on a proprietary multi-task corpus engineered for formal logic, multi-step deduction, and mathematical reasoning. The corpus combines examples from 47 reasoning sources with filtered synthetic examples.

In the evaluation described below, TwiL-LM achieved a macro-primary score of 0.361, compared with 0.185 for the unadapted SmolLM2-1.7B base. It led the sub-2B models included in this comparison. This result is specific to the reported formal-logic suite and should not be interpreted as a general measure of intelligence or performance on all reasoning tasks.

Intended uses

Direct use

• Translating English statements into first-order logic.
• Classifying whether a conclusion follows from a set of premises.
• Answering multiple-choice logic questions.
• Parsing natural language into structured representations.
• Drafting or critiquing Lean formalizations with external verification.
• Research and experimentation on small formal-reasoning models.

Downstream use

The model can be integrated into systems that combine language-model generation with symbolic solvers, theorem provers, schema validators, or human review. Downstream developers should validate outputs for their target domain and retain the license and safety restrictions of both this adapter and its base model.

Out-of-scope uses

• Autonomous medical, legal, financial, or safety-critical decisions.
• Treating generated FOL or Lean as verified without running an appropriate checker or theorem prover.
• Unsupervised deployment where logically incorrect or fabricated output can cause material harm.
• Uses prohibited by the webAI Non-Commercial License or the base-model license.
• Impersonation, deceptive systems, autonomous weaponry, or surveillance that targets protected classes.

Training details

Training data

The training corpus is proprietary and is not currently published as a Hugging Face dataset. It was assembled from 47 reasoning sources and augmented with synthetic examples. Samples were normalized into a shared schema, filtered for structural quality, deduplicated, and checked using a two-stage LLM verification process. Synthetic data can still contain undetected errors, stylistic artifacts, or verifier preferences.

Because the complete training corpus and all source-level mixture weights are not public, independent reproduction and contamination auditing are limited. Users should account for this limitation when comparing results or deploying the model.

Training procedure

• Method: Supervised fine-tuning with PEFT LoRA
• Rank: 64
• Alpha: 128
• Dropout: 0.0
• Bias: none
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj
• Task type: CAUSAL_LM

Optimizer settings, learning-rate schedule, epoch count, random seed, training hardware, wall-clock time, and energy consumption are not documented in the current release artifacts. These should be added when verified records become available.

Evaluation

Formal-logic evaluation

The in-domain evaluation used held-out samples with up to 200 rows per objective and greedy decoding. The reported macro-primary score is an equal-weight average over the eligible objectives. mcq_answer and procedural use max(accuracy, loose_match). lean_prove and perplexity corpora are excluded from the macro score.

The broader comparison includes checkpoints with different parameter counts, training procedures, and adaptation methods. It is useful for context but is not a controlled architecture or scaling study.

TwiL-LM performs best on entailment labeling (0.655) and improves FOL translation (0.235, compared with approximately zero for the base). Lean formalization, Lean proving, and procedural reasoning remain weak.

Out-of-Distribution (OOD) evaluation

OOD transfer was measured on GSM8K, ARC-Challenge chain-of-thought, ARC-Challenge 25-shot log-likelihood (acc_norm), and LogicBench BQA.

Compared with SmolLM2-1.7B, TwiL-LM improves LogicBench BQA (0.590 vs 0.563) but is lower on GSM8K (0.380 vs 0.413), ARC-C chain-of-thought (0.463 vs 0.587), and ARC-C 25-shot log-likelihood (0.460 vs 0.490).

These results indicate mixed transfer outside the formal-logic training distribution. Larger general reasoning models remain stronger on several OOD benchmarks.

Throughput evaluation

The formal-logic run generated 422,627 tokens in 1,152.34 generation seconds, or 366.8 aggregate tokens per second. The configuration used a maximum of 1,024 new tokens per example. Outputs averaged 264.1 new tokens; the median was 92 tokens, the 90th percentile was 1,024 tokens, and 16.1% of outputs reached the generation limit.

This is aggregate evaluation throughput, not single-request latency or a controlled serving benchmark. It depends on hardware, precision, backend, batching, prompt length, output length, and stopping behavior. The comparison runs did not consistently control all of these variables, so the figure should not be used to claim that LoRA intrinsically accelerates generation.

Evaluation limitations

• The formal-logic suite includes custom tasks and metrics and is not a standard Hugging Face benchmark dataset.
• Some comparison checkpoints used different output limits and evaluation runs.
• The evaluation primarily covers English.
• The current records do not provide confidence intervals or repeated-seed variance.
• Exact-match metrics can penalize semantically equivalent formal expressions.
• The training corpus is not public, limiting independent contamination checks.

Structured Hub evaluation files are not included because the internal suite is not registered as a Hugging Face Benchmark and the available records do not contain verified task IDs for the OOD benchmarks. The scores above are therefore reported in the card rather than submitted as verified Hub leaderboard results.

How to use

Installation and authentication

The model repository may require access approval. Install the dependencies and authenticate with the Hugging Face Hub:

pip install -U torch transformers peft accelerate huggingface_hub
hf auth login

Transformers with PEFT

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

base_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
adapter_id = "webAI-Official/TwIL-LM"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

messages = [
    {
        "role": "user",
        "content": "Translate to first-order logic: All cats are mammals.",
    }
]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        max_new_tokens=256,
        do_sample=False,
    )

new_tokens = output_ids[0, input_ids.shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Use the base model's tokenizer and chat template. Loading the adapter over a different base checkpoint is unsupported and can cause incorrect output or tensor shape errors.

llama.cpp with GGUF

Download the model repository:

hf download webAI-Official/TwIL-LM --local-dir TwIL-LM

If the release contains a GGUF LoRA adapter, apply it to a GGUF converted from the matching SmolLM2-1.7B-Instruct base:

llama-cli \
  -m /path/to/smollm2-1.7b-instruct-f16.gguf \
  --lora TwIL-LM/smollm2-lorav1-ps-lora-f16.gguf \
  -p "Translate to FOL: All cats are mammals." \
  -n 256

If the release contains a merged GGUF, use it without --lora:

llama-cli \
  -m TwIL-LM/smollm2-lorav1-ps-merged-Q4_K_M.gguf \
  -p "Translate to FOL: All cats are mammals." \
  -n 256

GGUF filenames can vary between releases. Confirm the downloaded filenames before running these commands.

Files and formats

• adapter_model.safetensors: PEFT LoRA weights, approximately 289 MB.
• adapter_config.json: PEFT configuration that identifies the required base model.
• smollm2-lorav1-ps-lora-f16.gguf: optional GGUF LoRA adapter, approximately 145 MB.
• Merged GGUF variants may be provided as FP16 (approximately 3.42 GB), Q8_0 (1.82 GB), Q5_K_M (1.23 GB), or Q4_K_M (1.06 GB).

Availability and filenames can vary by release. Quantized GGUF variants may not match the evaluation quality reported for the original adapter; validate the selected artifact on the target workload.

Limitations, risks, and biases

• Not a verifier: Plausible-looking FOL or Lean output can be syntactically or semantically wrong. Use a symbolic solver, Lean/Mathlib, or expert review.
• Narrow specialization: The adapter is designed for formal logic, not as a replacement for a general-purpose assistant.
• Mixed OOD performance: Improvements on LogicBench do not transfer consistently to GSM8K or ARC-Challenge.
• Small-model capacity: Long reasoning chains, deeply nested quantifiers, complex rule induction, and long formal contexts can fail or hallucinate.
• Synthetic-data artifacts: Synthetic examples and LLM verification can introduce systematic style, content, or verifier bias.
• English-first: Performance in other languages is not established.
• Base-model inheritance: The adapter retains the base model's limitations, biases, and potential for unsafe or inaccurate content.
• Context limit: Inputs near 8,192 tokens leave less room for generation and can be truncated by serving frameworks.
• Quantization effects: Q4 and Q5 builds may change formal-token generation and exact-match accuracy.
• No uncertainty calibration: Scores do not establish that model confidence corresponds to correctness.

Environmental impact

Training-energy use, hardware type, training duration, datacenter region, and carbon emissions were not recorded in the available release artifacts. No emissions estimate is provided. Inference impact varies with hardware, precision, quantization, sequence length, and utilization.

License

The model weights are distributed under the webAI Non-Commercial License ver. 1.0. Review that license and the SmolLM2 base-model terms before use. The license applied to source code in this repository does not override the model-weight license.

Citation

@misc{twil_lm_1_7b,
  title  = {TwiL-LM(1.7B) Formal-Logic LoRA},
  author = {webAI Intelligence Lab},
  year   = {2026},
  url    = {https://huggingface.co/webAI-Official/TwIL-LM}
}

Contact

For model questions, licensing requests, or reports of harmful behavior, use the Community tab of the webAI-Official/TwIL-LM repository.