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README.md

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+---
+language:
+- en
+- ru
+license: apache-2.0
+pipeline_tag: text-generation
+base_model: HuggingFaceTB/SmolLM2-360M
+datasets: nyuuzyou/EagleSFT
+co2_eq_emissions:
+  emissions: 5484 # in grams of CO2
+  source: "Calculated based on power consumption and regional carbon intensity"
+  training_type: "fine-tuning"
+  geographical_location: "Saint-Petersburg, Russia"
+  hardware_used: "1 RTX 4090 GPU"
+---
+# SmolLM2-360M-Eagle
+SmolLM2-360M-Eagle is a fine-tuned version of the [SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) 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: [SmolLM2-360M-Eagle-GGUF](https://huggingface.co/nyuuzyou/SmolLM2-360M-Eagle-GGUF)
+
+## Model Description
+SmolLM2-360M-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 SmolLM2-360M, 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**: 41h 14m total in Saint-Petersburg, Russia
+- **Power consumption**: 380W average
+- **Hardware**: 1 x RTX 4090
+- **Carbon emissions**: Approximately 5.48 kg CO2eq
+  - Calculated based on average power consumption and average CO2eq/kWh (350g) in this region
+  - Saint-Petersburg: 380W * 41.23h * 350g/kWh = 5.48 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 SmolLM2-360M model still apply

all_results.json

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+    "total_flos": 3.655598871565828e+18,
+    "train_loss": 0.5567794406680932,
+    "train_runtime": 148446.9136,
+    "train_samples": 1071440,
+    "train_samples_per_second": 2.123,
+    "train_steps_per_second": 0.265
+}

config.json

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+{
+  "_name_or_path": "HuggingFaceTB/SmolLM2-360M",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.46.3"
+}

model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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+}

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