barandinho/gpt-oss-120b-multilingual-reasoner-MXFP4

GPT-OSS-120B Multilingual Reasoner (with Turkish)

This model is a fine-tuned version of openai/gpt-oss-120b that can generate chain-of-thought reasoning in multiple languages, including Turkish.

Model Description

Large reasoning models like OpenAI o3 generate a chain-of-thought to improve the accuracy and quality of their responses. However, most of these models reason in English, even when a question is asked in another language.

This fine-tuned model addresses this limitation by adding a "reasoning language" option to the system prompt, enabling the model to think step-by-step in the user's preferred language. This improves interpretability for non-English speakers who want to understand the model's reasoning process.

Supported Reasoning Languages

• 🇺🇸 English
• 🇪🇸 Spanish
• 🇫🇷 French
• 🇮🇹 Italian
• 🇩🇪 German
• 🇹🇷 Turkish

Example Usage

Below is an example usage with unsloth but you can also use this model with HF, vLLM, SGLANG, llama.cpp.
Refer to your desired inference engine's gpt-oss documentation for further information

from unsloth import FastLanguageModel
from transformers import TextStreamer

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "barandinho/gpt-oss-120b-multilingual-reasoner-MXFP4", 
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

REASONING_LANGUAGE = "Turkish" # can be anything
DEVELOPER_PROMPT = "You are a helpful assistant. Respond in Turkish to the user." # using english here is recommended.
USER_PROMPT = "mükemmeliyetçilik kelimesinde kaç tane m harfi vardır ?"

messages = [
    {"role": "system", "content": f"reasoning language: {REASONING_LANGUAGE}\n\n{DEVELOPER_PROMPT}"}, # this should be the format of our system prompt.
    {"role": "user", "content": USER_PROMPT},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True,
    return_tensors = "pt",
    return_dict = True,
    reasoning_effort = "medium",
).to("cuda")

_ = model.generate(**inputs, max_new_tokens = 2048, temperature=1.0, top_p=1.0, top_k=0, do_sample=True, streamer=TextStreamer(tokenizer))

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-12-28

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: Turkish

You are a helpful assistant. Respond in Turkish to the user.<|end|><|start|>user<|message|>mükemmeliyetçilik kelimesinde kaç tane m harfi vardır ?<|end|><|start|>assistant<|channel|>analysis<|message|>Tamam, kullanıcı "mükemmeliyetçilik" kelimesinde kaç tane "m" harfi olduğunu soruyor. Adım adım düşünelim.

Öncelikle kelimeyi ayıralım ve her bir harfe bakalım. Şunu yazarak başlayabilirim: "mükemmeliyetçilik". Şimdi harfleri parçalara ayıralım:
- m
- ü
- k
- e
- m
- m
- e
- l
- i
- y
- e
- t
- ç
- i
- l
- i
- k

Verilen listede görünen "m" harfleri:
- Başlangıçta bir tane "m".
- Daha sonra "?m" olarak işaretlenmiş bir öğe var.
- Ayrıca bir tane daha "m" var.

Öyleyse toplam üç tane "m" harfi bulunuyor.

Dolayısıyla, kullanıcı için cevap basit ve açıklansız olabilir. Sonuçlanan söyle? Hayır, "mükemmeliyetçilik" kelimesinde 3 "m" harfi vardır.<|end|><|start|>assistant<|channel|>final<|message|>"Mükemmeliyetçilik" kelimesinde 3 tane **'m'** harfi bulunur. <|return|>

Training Details

Dataset

This model was fine-tuned on barandinho/Multilingual-Thinking-with-Turkish-1185, an extended version of HuggingFaceH4/Multilingual-Thinking that includes Turkish reasoning examples.

• Total examples: 1,185
• Languages: English, Spanish, French, Italian, German, Turkish

Training Configuration

Parameter	Value
Base Model	gpt-oss-120b
Fine-tuning Method	LoRA (via Unsloth)
LoRA Rank (r)	16
LoRA Alpha	32
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable Parameters	11,943,936 (0.01% of 116B)
Max Sequence Length	3,072
Quantization (Training)	4-bit

Training Hyperparameters

Parameter	Value
Learning Rate	2e-4
Batch Size (per device)	1
Gradient Accumulation Steps	4
Effective Batch Size	4
Epochs	1
Warmup Ratio	0.05
LR Scheduler	Linear
Optimizer	AdamW 8-bit
Weight Decay	0.001
Total Steps	297

Training Infrastructure

• Hardware: NVIDIA H100 80GB
• Training Time: ~3 hours
• Framework: Unsloth + TRL

Related Models

• Lora Adapter Version: barandinho/gpt-oss-120b-multilingual-reasoner - unmerged lora adapter version of the model.

Acknowledgments

This work is inspired by the OpenAI Cookbook tutorial on fine-tuning gpt-oss-20b

Special thanks to :

• OpenAI for releasing the gpt-oss model family
• HuggingFace for the TRL library and Multilingual-Thinking dataset
• Unsloth for efficient fine-tuning optimizations
• TRUBA for providing H100 GPU infrastructure

Limitations

• The model has been fine-tuned on a relatively small dataset (1,185 examples), so performance may vary across different reasoning tasks.
• While the model can generalize to other languages not explicitly in the training set, quality may be lower compared to the six supported languages.
• Complex mathematical or scientific reasoning performance may be degraded.

Citation

If you use this model, please cite:

@misc{gpt-oss-120b-multilingual-reasoner,
  author = {Baran Bingöl},
  title = {GPT-OSS-120B Multilingual Reasoner with Turkish},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/barandinho/gpt-oss-120b-multilingual-reasoner}
}

License

This model inherits the license from the base model openai/gpt-oss-120b. Please refer to the base model's license for usage terms.