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---
language:
- en
library_name: peft
pipeline_tag: text-generation
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
license: other
license_name: webai-non-commercial-license-ver.-1.0
license_link: https://huggingface.co/webAI-Official/webAI-ColVec1-4b/blob/main/LICENSE.md
tags:
- formal-logic
- reasoning
- lora
- peft
- gguf
- llama.cpp
- smollm2
- twil-lm
---
# TwiL-LM(1.7B)
TwiL-LM(1.7B) is a parameter-efficient LoRA adapter for
[SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/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](https://huggingface.co/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.
![TwiL-LM data creation, training, and evaluation workflow](card_assets/data_workflow_v2.png)
## 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.
![Macro accuracy compared with base models](card_assets/macro_vs_bases.png)
![Macro accuracy compared across Base and LoRA models](card_assets/macro_vs_bases_and_lora.png)
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 compared with SmolLM2-1.7B by objective](card_assets/objectives_vs_smollm2.png)
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.
![TwiL-LM and SmolLM2-1.7B on OOD benchmarks](card_assets/unseen_benchmarks.png)
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**).
![OOD benchmark macro accuracy for Base, FT, and PS variants](card_assets/unseen_macro_no_gsm8k.png)
These results indicate mixed transfer outside the formal-logic training
distribution. Larger general reasoning models remain stronger on several OOD
benchmarks.
### Throughput evaluation
![Aggregate generation throughput](card_assets/throughput.png)
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:
```bash
pip install -U torch transformers peft accelerate huggingface_hub
hf auth login
```
### Transformers with PEFT
```python
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:
```bash
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:
```bash
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`:
```bash
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](https://huggingface.co/webAI-Official/webAI-ColVec1-4b/blob/main/LICENSE.md).
Review that license and the
[SmolLM2 base-model terms](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
before use. The license applied to source code in this repository does not
override the model-weight license.
## Citation
```bibtex
@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](https://huggingface.co/webAI-Official/TwIL-LM)
repository.

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chat_template.jinja Normal file
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
' }}{% endif %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}

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config.json Normal file
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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"torch_dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 24,
"num_key_value_heads": 32,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 130000,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers.js_config": {
"torch_dtype": "q4",
"kv_cache_dtype": {
"fp16": "float16",
"q4f16": "float16"
},
"use_external_data_format": {
"model.onnx": true,
"model_fp16.onnx": true
}
},
"unsloth_version": "2026.7.4",
"use_cache": true,
"vocab_size": 49152
}

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generation_config.json Normal file
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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": [
2
],
"max_length": 8192,
"pad_token_id": 0,
"transformers_version": "5.5.0"
}

244965
tokenizer.json Normal file
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157
tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|im_start|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"is_local": false,
"model_max_length": 8192,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152,
"added_tokens_decoder": {
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"single_word": false,
"lstrip": false,
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"normalized": false,
"special": true
},
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"special": true
},
"2": {
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"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
"3": {
"content": "<repo_name>",
"single_word": false,
"lstrip": false,
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"normalized": false,
"special": true
},
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"single_word": false,
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},
"5": {
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"special": true
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"7": {
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"single_word": false,
"lstrip": false,
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"normalized": false,
"special": true
},
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"single_word": false,
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},
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"single_word": false,
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"normalized": false,
"special": true
},
"16": {
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"single_word": false,
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"rstrip": false,
"normalized": false,
"special": true
}
}
}