Tensor-2-40m-instruct

Tensor-2-40m-instruct is a Russian-language language model from the Tensor series, developed as part of the GribAI project. This is an instruction-tuned version of Tensor-2-40m-base, fine-tuned to follow instructions and hold a dialogue.

Description

Built on top of Tensor-2-40m-base, this model was additionally fine-tuned on a 150 MB SFT (supervised fine-tuning) dataset, including code-related data. As a result, it follows instructions more reliably and handles code-related prompts better than the base model.

Training

• Base model: Tensor-2-40m-base
• SFT dataset: 150 MB, including code
• Stage: supervised fine-tuning (SFT)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "VGribAI/Tensor-2-40m-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Напиши функцию на Python, которая считает факториал числа"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

As a small model, it may still make mistakes in complex reasoning, long-context tasks, or less common domains. Always verify generated code before running it. GribAI project (VGribAI).