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---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- safetensors
- onnx
- transformers.js
- heretic
- uncensored
- decensored
- abliterated
- reproducible
base_model:
- HuggingFaceTB/SmolLM2-135M
---
# This is a decensored version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.3.0
> [!TIP]
> **This model is reproducible!**
>
> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
## Abliteration parameters
| Parameter | Value |
| :-------- | :---: |
| **direction_index** | 14.68 |
| **attn.o_proj.max_weight** | 0.93 |
| **attn.o_proj.max_weight_position** | 19.53 |
| **attn.o_proj.min_weight** | 0.28 |
| **attn.o_proj.min_weight_distance** | 9.61 |
| **mlp.down_proj.max_weight** | 1.10 |
| **mlp.down_proj.max_weight_position** | 20.78 |
| **mlp.down_proj.min_weight** | 0.67 |
| **mlp.down_proj.min_weight_distance** | 3.29 |
## Performance
| Metric | This model | Original model ([HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)) |
| :----- | :--------: | :---------------------------: |
| **KL divergence** | 0.0373 | 0 *(by definition)* |
| **Refusals** | 1/50 | 4/50 |
-----
# SmolLM2
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/3ntM63zkmxY2cNRhgY_Kl.png)
## Table of Contents
1. [Model Summary](##model-summary)
2. [Limitations](##limitations)
3. [Training](##training)
4. [License](##license)
5. [Citation](##citation)
## Model Summary
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper https://arxiv.org/abs/2502.02737
SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
The instruct model additionally supports tasks such as text rewriting, summarization and function calling (for the 1.7B) thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1).
You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk and finetuning code at https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
### How to use
### Transformers
```bash
pip install transformers
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "What is gravity?"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
```
### Chat in TRL
You can also use the TRL CLI to chat with the model from the terminal:
```bash
pip install trl
trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu
```
### Transformers.js
```bash
npm i @huggingface/transformers
```
```js
import { pipeline } from "@huggingface/transformers";
// Create a text generation pipeline
const generator = await pipeline(
"text-generation",
"HuggingFaceTB/SmolLM2-135M-Instruct",
);
// Define the list of messages
const messages = [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What is the capital of France?" },
];
// Generate a response
const output = await generator(messages, { max_new_tokens: 128 });
console.log(output[0].generated_text.at(-1).content);
// "The capital of France is Paris."
```
## Evaluation
In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.
## Base pre-trained model
| Metrics | SmolLM2-135M-8k | SmolLM-135M |
|:-------------------|:----------------:|:------------:|
| HellaSwag | **42.1** | 41.2 |
| ARC (Average) | **43.9** | 42.4 |
| PIQA | 68.4 | 68.4 |
| MMLU (cloze) | **31.5** | 30.2 |
| CommonsenseQA | **33.9** | 32.7 |
| TriviaQA | 4.1 | **4.3** |
| Winogrande | 51.3 | 51.3 |
| OpenBookQA | **34.6** | 34.0 |
| GSM8K (5-shot) | **1.4** | 1.0 |
## Instruction model
| Metric | SmolLM2-135M-Instruct | SmolLM-135M-Instruct |
|:-----------------------------|:---------------------:|:--------------------:|
| IFEval (Average prompt/inst) | **29.9** | 17.2 |
| MT-Bench | **19.8** | 16.8 |
| HellaSwag | **40.9** | 38.9 |
| ARC (Average) | **37.3** | 33.9 |
| PIQA | **66.3** | 64.0 |
| MMLU (cloze) | **29.3** | 28.3 |
| BBH (3-shot) | **28.2** | 25.2 |
| GSM8K (5-shot) | 1.4 | 1.4 |
## Limitations
SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
## Training
### Model
- **Architecture:** Transformer decoder
- **Pretraining tokens:** 2T
- **Precision:** bfloat16
### Hardware
- **GPUs:** 64 H100
### Software
- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main)
## License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Citation
```bash
@misc{allal2025smollm2smolgoesbig,
title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf},
year={2025},
eprint={2502.02737},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.02737},
}
```

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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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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"initializer_range": 0.041666666666666664,
"intermediate_size": 1536,
"is_llama_config": true,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"pad_token_id": 2,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_interleaved": false,
"rope_parameters": {
"rope_theta": 100000,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers.js_config": {
"kv_cache_dtype": {
"fp16": "float16",
"q4f16": "float16"
}
},
"transformers_version": "5.6.2",
"use_cache": true,
"vocab_size": 49152
}

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"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 2,
"transformers_version": "5.6.2"
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# Reproduction guide
This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.
> [!WARNING]
> **Local code**
>
> This system installed Heretic from a local directory or wheel. Uncommitted or experimental code may have been executed.
>
> Reproducibility *cannot* be guaranteed in this environment.
## Models
- **Base model:** [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) (Commit: [`12fd25f`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct/commit/12fd25f77366fa6b3b4b768ec3050bf629380bac))
## Datasets
- **Good prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
- **Bad prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
- **Good evaluation prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
- **Bad evaluation prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
## Selected trial
- **Trial number:** 2
- **KL divergence:** 0.037255
- **Refusals:** 1/50
## System
- **Python:** 3.12.10 (CPython, MSC v.1943 64 bit (AMD64)) [Virtualenv/Venv]
- **Operating system:** Windows-11-10.0.26200-SP0 (AMD64)
- **CPU:** 13th Gen Intel(R) Core(TM) i7-13700HX
### Accelerators
- **CUDA:** Detected 1 device(s) (8.00 GB total VRAM)
- **CUDA Version:** 12.8
- **Driver Version:** 581.80
- **Devices:**
- **CUDA 0:** NVIDIA GeForce RTX 4060 Laptop GPU (8.00 GB)
## Environment
- **Heretic:** v1.3.0 (Origin: Local)
- **PyTorch:** 2.11.0+cu128
- **Other dependencies:** See [`requirements.txt`](requirements.txt).
## Contents of this directory
- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
- [`HuggingFaceTB--SmolLM2-135M-Instruct.jsonl`](HuggingFaceTB--SmolLM2-135M-Instruct.jsonl): The Optuna study journal containing the history of all trials.
- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
## How to reproduce
> [!TIP]
> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
> `heretic --reproduce reproduce.json`.
1. Ensure your system matches the specifications in the **System** section above. Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.
1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
1. Install the correct version of PyTorch: `pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128`
1. Place the provided `config.toml` in your working directory.
1. Run Heretic without any additional arguments: `heretic`
1. Wait for the run to finish, then select trial **2** and export the model.
1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`:
`sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
> [!TIP]
> To use the included Optuna study journal `HuggingFaceTB--SmolLM2-135M-Instruct.jsonl`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
>
> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.

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4d3c0d7cc4da4655062d76bfcd9bc5b00115687adc57d852b23f599c1545c190 *model.safetensors

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model = "HuggingFaceTB/SmolLM2-135M-Instruct"
model_commit = "12fd25f77366fa6b3b4b768ec3050bf629380bac"
dtypes = [
"auto",
"float16",
"bfloat16",
"float32",
]
quantization = "none"
device_map = "auto"
offload_outputs_to_cpu = true
batch_size = 16
max_response_length = 32
response_prefix = ""
kl_divergence_scale = 1.0
kl_divergence_target = 0.0
orthogonalize_direction = true
row_normalization = "full"
full_normalization_lora_rank = 3
winsorization_quantile = 1.0
n_trials = 4
n_startup_trials = 60
seed = 42
max_shard_size = "5GB"
export_strategy = "merge"
refusal_markers = [
"disclaimer",
"sorry",
"i can'",
"i cant",
"i cannot",
"i won'",
"i wont",
"i will not",
"i unable",
"im unable",
"i'm unable",
"i am unable",
"i an ai",
"im an ai",
"i'm an ai",
"i am an ai",
"as an ai",
"ai assistant",
"i designed to",
"im designed to",
"i'm designed to",
"i am designed to",
"i programmed to",
"im programmed to",
"i'm programmed to",
"i am programmed to",
"violat",
"prohibit",
"illegal",
"harmful",
"inappropriate",
"unethical",
"ethical boundaries",
]
system_prompt = "You are a helpful assistant."
[good_prompts]
dataset = "mlabonne/harmless_alpaca"
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
split = "train[:128]"
column = "text"
prefix = ""
suffix = ""
[bad_prompts]
dataset = "mlabonne/harmful_behaviors"
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
split = "train[:128]"
column = "text"
prefix = ""
suffix = ""
[good_evaluation_prompts]
dataset = "mlabonne/harmless_alpaca"
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
split = "test[:50]"
column = "text"
prefix = ""
suffix = ""
[bad_evaluation_prompts]
dataset = "mlabonne/harmful_behaviors"
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
split = "test[:50]"
column = "text"
prefix = ""
suffix = ""

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{
"version": "2",
"timestamp": "2026-06-11T22:08:23",
"system": {
"python": {
"version": "3.12.10",
"implementation": "CPython",
"compiler": "MSC v.1943 64 bit (AMD64)",
"environment": "Virtualenv/Venv"
},
"os": {
"platform": "Windows-11-10.0.26200-SP0",
"machine": "AMD64"
},
"cpu": {
"brand": "13th Gen Intel(R) Core(TM) i7-13700HX",
"vendor": "GenuineIntel",
"family": 6,
"model": 183,
"stepping": 1
},
"accelerators": {
"type": "CUDA",
"api_name": "CUDA Version",
"api_version": "12.8",
"driver_version": "581.80",
"devices": [
{
"name": "NVIDIA GeForce RTX 4060 Laptop GPU",
"vram_gb": 8.0
}
]
}
},
"environment": {
"heretic": {
"version": "1.3.0",
"is_standard_pypi": false,
"metadata": {
"type": "local"
}
},
"pytorch_version": "2.11.0+cu128",
"requirements": {
"absl-py": "2.4.0",
"accelerate": "1.13.0",
"alembic": "1.17.2",
"annotated-doc": "0.0.4",
"annotated-types": "0.7.0",
"anyio": "4.12.0",
"attrs": "25.4.0",
"bitsandbytes": "0.49.2",
"certifi": "2025.11.12",
"chardet": "5.2.0",
"charset-normalizer": "3.4.4",
"click": "8.3.1",
"colorama": "0.4.6",
"colorlog": "6.10.1",
"dataproperty": "1.1.0",
"datasets": "4.8.4",
"dill": "0.4.0",
"evaluate": "0.4.6",
"filelock": "3.20.3",
"fsspec": "2025.10.0",
"greenlet": "3.3.0",
"h11": "0.16.0",
"hf-xet": "1.4.2",
"httpcore": "1.0.9",
"httpx": "0.28.1",
"huggingface-hub": "1.7.2",
"idna": "3.15",
"immutabledict": "4.3.1",
"jinja2": "3.1.6",
"joblib": "1.5.2",
"jsonlines": "4.0.0",
"langdetect": "1.0.9",
"lm-eval": "0.4.11",
"lxml": "6.0.2",
"mako": "1.3.12",
"markdown-it-py": "4.0.0",
"markupsafe": "3.0.3",
"mbstrdecoder": "1.1.4",
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}

102
reproduce/requirements.txt Normal file
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absl-py==2.4.0
accelerate==1.13.0
alembic==1.17.2
annotated-doc==0.0.4
annotated-types==0.7.0
anyio==4.12.0
attrs==25.4.0
bitsandbytes==0.49.2
certifi==2025.11.12
chardet==5.2.0
charset-normalizer==3.4.4
click==8.3.1
colorama==0.4.6
colorlog==6.10.1
dataproperty==1.1.0
datasets==4.8.4
dill==0.4.0
evaluate==0.4.6
filelock==3.20.3
fsspec==2025.10.0
greenlet==3.3.0
h11==0.16.0
hf-xet==1.4.2
httpcore==1.0.9
httpx==0.28.1
huggingface-hub==1.7.2
idna==3.15
immutabledict==4.3.1
jinja2==3.1.6
joblib==1.5.2
jsonlines==4.0.0
langdetect==1.0.9
lm-eval==0.4.11
lxml==6.0.2
mako==1.3.12
markdown-it-py==4.0.0
markupsafe==3.0.3
mbstrdecoder==1.1.4
mdurl==0.1.2
more-itertools==10.8.0
mpmath==1.3.0
multiprocess==0.70.18
networkx==3.6.1
nltk==3.9.4
numpy==2.3.5
optuna==4.8.0
packaging==25.0
pandas==2.3.3
pathvalidate==3.3.1
peft==0.19.1
pillow==12.2.0
portalocker==3.2.0
prompt-toolkit==3.0.52
psutil==7.2.2
py-cpuinfo==9.0.0
pyarrow==23.0.1
pydantic==2.12.5
pydantic-core==2.41.5
pydantic-settings==2.13.1
pygments==2.20.0
pytablewriter==1.2.1
python-dateutil==2.9.0.post0
python-dotenv==1.2.2
pytz==2025.2
pywin32==311
pyyaml==6.0.3
questionary==2.1.1
regex==2025.11.3
requests==2.33.0
rich==14.3.3
rouge-score==0.1.2
sacrebleu==2.6.0
safetensors==0.7.0
scikit-learn==1.8.0
scipy==1.16.3
setuptools==80.9.0
shellingham==1.5.4
six==1.17.0
sqlalchemy==2.0.45
sqlitedict==2.1.0
sympy==1.14.0
tabledata==1.3.4
tabulate==0.10.0
tcolorpy==0.1.7
threadpoolctl==3.6.0
tokenizers==0.22.1
tomli-w==1.2.0
torch==2.11.0
torchaudio==2.11.0
torchvision==0.26.0
tqdm==4.67.1
transformers==5.6.2
typepy==1.3.4
typer==0.24.1
typing-extensions==4.15.0
typing-inspection==0.4.2
tzdata==2025.2
urllib3==2.7.0
wcwidth==0.2.14
word2number==1.1
xxhash==3.6.0
zstandard==0.25.0

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tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
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"eos_token": "<|im_end|>",
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],
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}