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Model: nyuuzyou/SmolLM2-1.7B-Eagle-GGUF Source: Original Platform
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README.md
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---
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language:
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- en
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- ru
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license: apache-2.0
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pipeline_tag: text-generation
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base_model: nyuuzyou/SmolLM2-1.7B-Eagle
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datasets: nyuuzyou/EagleSFT
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co2_eq_emissions:
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emissions: 11163 # in grams of CO2
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source: "Calculated based on power consumption and regional carbon intensity"
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training_type: "fine-tuning"
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geographical_location: "Kazan, Russia"
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hardware_used: "1 RTX 5090 GPU"
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---
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# SmolLM2-1.7B-Eagle
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SmolLM2-1.7B-Eagle-GGUF is a GGUF conversion of the [SmolLM2-1.7B-Eagle](https://huggingface.co/nyuuzyou/SmolLM2-1.7B-Eagle) model, which itself is a fine-tuned version of [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B) on the [EagleSFT](https://huggingface.co/datasets/nyuuzyou/EagleSFT) dataset. This model is designed to improve capabilities in both Russian and English language tasks while being optimized for efficient local deployment.
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## Model Description
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SmolLM2-1.7B-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.
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### Base Model
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The model is built upon SmolLM2-1.7B, a compact language model with 360 million parameters that offers a good balance between performance and resource requirements.
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## Fine-tuning Details
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### Dataset
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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.
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### Environmental Impact
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- **Training duration**: 79.73h total in Kazan, Russia
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- **Power consumption**: 400W average
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- **Hardware**: 1 x RTX 5090
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- **Carbon emissions**: Approximately 11.16 kg CO2eq
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- Calculated based on average power consumption and average CO2eq/kWh (350g) in this region
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- Kazan: 400W * 79.73h * 350g/kWh = 11.16 kg CO2eq
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### Training Parameters
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- **Training approach**: Supervised Fine-Tuning (SFT)
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- **Training epochs**: 2
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- **Learning rate**: 3.0e-04
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- **Precision**: bfloat16
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## Limitations and Capabilities
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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.
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Despite extensive limitations, the model shows minimal improvement in:
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- Basic recognition of Russian prompts (though with frequent misunderstandings)
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- Handling simple tasks formatted as "{question in Russian}, answer in English"
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- Basic translation from Russian to English (though quality remains poor)
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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.
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## Experimental Capabilities
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The model demonstrates some experimental capabilities, but with significant limitations:
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- Basic Russian text understanding (with frequent errors and misinterpretations)
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- Limited question answering in Russian (quality significantly lower than English)
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- Basic Russian to English translation (better than English to Russian)
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## Limitations
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- **NOT SUITABLE FOR PRODUCTION USE**: This model should not be used in production environments in any form
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- Extremely limited knowledge base for Russian language due to lack of pre-training with Russian text
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- Unoptimized tokenizer performance for Russian language results in inefficient token usage
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- Output quality in Russian will be unsatisfactory for most use cases
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- May produce inaccurate, inconsistent, or inappropriate responses, especially in Russian
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- All limitations of the base SmolLM2-1.7B model still apply
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