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README.md

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+---
+language:
+- en
+- ru
+license: apache-2.0
+pipeline_tag: text-generation
+base_model: HuggingFaceTB/SmolLM2-1.7B
+datasets: nyuuzyou/EagleSFT
+co2_eq_emissions:
+  emissions: 11163 # in grams of CO2
+  source: "Calculated based on power consumption and regional carbon intensity"
+  training_type: "fine-tuning"
+  geographical_location: "Kazan, Russia"
+  hardware_used: "1 RTX 5090 GPU"
+---
+# SmolLM2-1.7B-Eagle
+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.
+
+## Model Description
+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.
+
+### Base Model
+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.
+
+## 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**: 79.73h total in Kazan, Russia
+- **Power consumption**: 400W average
+- **Hardware**: 1 x RTX 5090
+- **Carbon emissions**: Approximately 11.16 kg CO2eq
+  - Calculated based on average power consumption and average CO2eq/kWh (350g) in this region
+  - Kazan: 400W * 79.73h * 350g/kWh = 11.16 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 SmolLM2-1.7B model still apply

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