---
language:
- en
- ru
license: apache-2.0
pipeline_tag: text-generation
base_model: HuggingFaceTB/SmolLM2-135M
datasets: nyuuzyou/EagleSFT
co2_eq_emissions:
  emissions: 3110 # in grams of CO2
  source: "Calculated based on power consumption and regional carbon intensity"
  training_type: "fine-tuning"
  geographical_location: "Tyumen, Russia and Saint-Petersburg, Russia"
  hardware_used: "1 RTX 4090 GPU"
---

# SmolLM2-135M-Eagle

SmolLM2-135M-Eagle is a fine-tuned version of the [SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) model on the [EagleSFT](https://huggingface.co/datasets/nyuuzyou/EagleSFT) dataset, designed to improve the model's capabilities in both Russian and English language tasks.

GGUF version of this model is available at: [SmolLM2-135M-Eagle-GGUF](https://huggingface.co/nyuuzyou/SmolLM2-135M-Eagle-GGUF)

## Model Description

SmolLM2-135M-Eagle is a lightweight language model that has been fine-tuned specifically to handle bilingual content. This fine-tuning extends the base model's capabilities to better understand and generate content in Russian while maintaining its English competency.

### Base Model

The model is built upon SmolLM2-135M, a compact language model with 135 million parameters that offers a good balance between performance and resource requirements.

## Fine-tuning Details

### Dataset

The model was fine-tuned on the EagleSFT dataset, which contains 536,231 pairs of human questions and machine-generated responses in both Russian and English languages. The dataset primarily focuses on educational content but also includes everyday questions and casual conversations.

### Environmental Impact

- **Training duration**: 26h 26m total
  - 15h 19m 52s in Tyumen, Russia (300W power consumption)
  - 11h 6m 8s in Saint-Petersburg (360W power consumption)
- **Hardware**: 1 x RTX 4090
- **Carbon emissions**: Approximately 3.11 kg CO2eq
  - Calculated based on average power consumption and average CO2eq/kWh (350g) in these regions
  - Tyumen: 300W * 15.33h * 350g/kWh = 1.61 kg CO2eq
  - Saint-Petersburg: 360W * 11.10h * 350g/kWh = 1.50 kg CO2eq

### Training Parameters

- **Training approach**: Supervised Fine-Tuning (SFT)
- **Training epochs**: 2
- **Learning rate**: 3.0e-04
- **Precision**: bfloat16

## Limitations and Capabilities

It's important to note that this model was not pre-trained but only underwent SFT on a relatively small number of tokens. This means that the model has a limited amount of data to rely on when answering in Russian compared to its English capabilities.

Despite extensive limitations, the model shows minimal improvement in:
- Basic recognition of Russian prompts (though with frequent misunderstandings)
- Handling simple tasks formatted as "{question in Russian}, answer in English"
- Basic translation from Russian to English (though quality remains poor)

The model's minimal understanding of Russian language comes solely from the supervised fine-tuning process without any proper pre-training with Russian text corpus, resulting in severely limited capabilities.

## Experimental Capabilities

The model demonstrates some experimental capabilities, but with significant limitations:
- Basic Russian text understanding (with frequent errors and misinterpretations)
- Limited question answering in Russian (quality significantly lower than English)
- Basic Russian to English translation (better than English to Russian)

## Limitations

- **NOT SUITABLE FOR PRODUCTION USE**: This model should not be used in production environments in any form
- Extremely limited knowledge base for Russian language due to lack of pre-training with Russian text
- Unoptimized tokenizer performance for Russian language results in inefficient token usage
- Output quality in Russian will be unsatisfactory for most use cases
- May produce inaccurate, inconsistent, or inappropriate responses, especially in Russian
- All limitations of the base SmolLM2-135M model still apply