license: apache-2.0
base_model: GLM-4.7-Flash
tags:
- math
- reasoning
- chain-of-thought
- fine-tuned
- glm
- unsloth
datasets:
- unsloth/OpenMathReasoning-mini
language:
- en
pipeline_tag: text-generation
🍡 Mochi
Mochi is a math-reasoning fine-tune of GLM-4.7-Flash, trained on the Open Math Reasoning (mini) dataset — the same chain-of-thought data used in the winning submission to the AI Mathematical Olympiad Progress Prize 2 (AIMO-2) on Kaggle.
The goal of this fine-tune is to sharpen GLM-4.7-Flash's step-by-step mathematical reasoning while keeping the small, fast footprint of the Flash base model.
Looking for a quantized/local version? See mochi-gguf for GGUF builds you can run with llama.cpp, Ollama, or LM Studio.
Model Details
- Base model: GLM-4.7-Flash
- Fine-tuning data: unsloth/OpenMathReasoning-mini — chain-of-thought math solutions distilled from the larger OpenMathReasoning dataset, which underpinned NVIDIA's AIMO-2-winning system
- Objective: Improve multi-step mathematical reasoning and answer accuracy on olympiad-style problems
- Language(s): English
- License: apache-2.0 (inherits from GLM-4.7-Flash — please verify against the base model's license before commercial use)
Intended Use
Mochi is intended for:
- Step-by-step mathematical problem solving (algebra, number theory, combinatorics, olympiad-style questions)
- Research into chain-of-thought fine-tuning on small/flash-class models
- Local or low-latency deployments that still need decent math reasoning
It is not intended as a general-purpose reasoning or safety-critical decision-making tool. As with any LLM, verify important calculations independently.
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "artindnr/mochi"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "If x^2 - 5x + 6 = 0, what are the values of x?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
You can also load Mochi with Unsloth for faster inference and further fine-tuning:
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="artindnr/mochi",
max_seq_length=4096,
load_in_4bit=True,
)
Training
Mochi was fine-tuned starting from GLM-4.7-Flash on the CoT split of unsloth/OpenMathReasoning-mini, using Unsloth for efficient LoRA/QLoRA training.
| Base model | GLM-4.7-Flash |
| Dataset | unsloth/OpenMathReasoning-mini (CoT split) |
| Task | Supervised fine-tuning (chain-of-thought math) |
| Framework | Unsloth |
Limitations
- Fine-tuned specifically for math reasoning; general chat/instruction-following ability may drift from the base model.
- Trained on a "mini" subset of OpenMathReasoning, so coverage of problem types is narrower than the full dataset.
- Like all LLMs, it can produce confidently incorrect derivations — always double-check final answers on problems that matter.
Citation
If you use this model, please also credit the underlying dataset and competition it draws from:
@misc{openmathreasoning,
title = {OpenMathReasoning},
author = {NVIDIA},
year = {2025},
url = {https://huggingface.co/datasets/nvidia/OpenMathReasoning}
}
Acknowledgements
- GLM-4.7-Flash for the base model
- Unsloth for fine-tuning tooling
- NVIDIA's AIMO-2 team for the OpenMathReasoning dataset