kurakurai

Luth-2-2B

README

License: apache-2.0

Model variants

Table with columns: Model, Description
Model	Description
Luth-2-2B	Original checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM and SGLang.
Luth-2-2B-GGUF	Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and reduced memory usage.

Training

Luth-2-2B is post-trained from Qwen3.5-2B in two stages:

Supervised fine-tuning on Luth-2-Post-Training-SFT, a 3B-token French mixture spanning math (37.2%), knowledge (27.9%), code (22.2%), instruction following (6.5%) and tool calling (6.3%). Prompts were translated from English SFT datasets and answers regenerated with strong open-source teachers.
Multi-domain on-policy distillation (MOPD). Three specialists (math, code, instruction following) are trained separately with GRPO on Luth-2-Post-Training-RL, then distilled back into the SFT student.

Inference

Luth-2-2B is supported by Transformers, vLLM, SGLang and more.

Quick start with Transformers:

python

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamermodel_id = "kurakurai/Luth-2-2B"model = AutoModelForCausalLM.from_pretrained(    model_id,    device_map="auto",    dtype="bfloat16",    # attn_implementation="flash_attention_2"  # uncomment on compatible GPU)tokenizer = AutoTokenizer.from_pretrained(model_id)streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)prompt = "Quelle est la capitale de la France?"input_ids = tokenizer.apply_chat_template(    [{"role": "user", "content": prompt}],    add_generation_prompt=True,    return_tensors="pt",    tokenize=True,)["input_ids"].to(model.device)output = model.generate(    input_ids,    do_sample=True,    temperature=0.8,    top_p=0.95,    top_k=20,    max_new_tokens=512,    streamer=streamer,)

Evaluation

Evaluations can be reproduced using our GitHub repository. The benchmarks are French subsets or verified translations, scored with temperature=0.6, top_p=0.95, top_k=20, thinking disabled, averaged over 10 runs.

Table with columns: French Benchmarks, Luth-2-2B, Luth-1.7B-Instruct, Qwen3.5-2B
French Benchmarks	Luth-2-2B	Luth-1.7B-Instruct	Qwen3.5-2B
MGSM-rev2	86.52	75.72	64.60
AIME 24	17.00	10.33	11.67
AIME 25	19.00	7.00	6.67
Math-500	81.52	65.26	65.06

See the French LLM Leaderboard for comparisons across models.

Contact

Questions or feedback? Reach us on LinkedIn: Maxence Lasbordes and Guillaume Pradel.

Citation

bibtex

@misc{luth2,  title  = {Luth-2: Pushing the French Capabilities of SLMs with MOPD},  author = {Maxence Lasbordes and Guillaume Pradel},  year   = {2026},  url    = {https://huggingface.co/blog/MaxLSB/luth-2}}

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Model Details

Model Provider

kurakurai

Model Tree

Base

Qwen/Qwen3.5-2B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality