cb316fc1ed
Model: Yoro9381/LFM2.5-1.2B-Instruct-Korean-Opus-4.6-Distill-GGUF Source: Original Platform
114 lines
4.8 KiB
Markdown
114 lines
4.8 KiB
Markdown
---
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base_model: Yoro9381/LFM2.5-1.2B-Instruct-Korean-Opus-4.6-Distill
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library_name: transformers
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pipeline_tag: text-generation
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license: other
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license_name: lfm-open-license-v1.0
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license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE
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tags:
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- unsloth
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- lfm2
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- lfm2.5
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- korean
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- text-generation
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- conversational
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- instruction-tuned
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datasets:
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- Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2
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language:
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- ko
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---
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# LFM2.5-1.2B-Instruct-Korean
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## Model Overview
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`LFM2.5-1.2B-Instruct-Korean` is a Korean instruction-following language model based on `LiquidAI/LFM2.5-1.2B-Instruct`.
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This model was fine-tuned on Korean-centered datasets with the goal of improving performance on Korean question answering, general conversation, and instruction-following tasks.
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The model is designed to generate responses that are more natural, consistent, and contextually appropriate in Korean.
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## Base Model
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- **Base model**: `LiquidAI/LFM2.5-1.2B-Instruct`
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## Training Data
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This model was fine-tuned using the following Korean-centered datasets:
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- `maywell/koVast`
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- `CarrotAI/ko-instruction-dataset`
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- `MarkrAI/KoCommercial-Dataset`
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- `Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2`
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The training data includes Korean instruction-response pairs, general conversational data, and commercially or practically oriented text.
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This setup was intended to help the model learn a broad range of Korean expressions, styles, and contexts.
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## Training Details
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The model was trained for **3 full epochs** over the entire dataset.
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Training proceeded for a total of **2,160 steps** and was completed successfully without interruption.
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### Training Summary
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- **Number of epochs**: 3
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- **Total training steps**: 2,160
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- **Training completion status**: Completed
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- **Training status**: Stable
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## Evaluation Results
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The final evaluation metrics are as follows:
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- **training_loss**: 0.9999
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- **eval_loss**: 1.0480
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- **eval_mean_token_accuracy**: 0.7445
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These results show that the training loss and evaluation loss remained at nearly the same level, suggesting that the model demonstrated relatively stable generalization performance on the validation set without a clear sign of overfitting.
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## Result Interpretation
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One notable point in this experiment is the very small gap between **training loss** and **evaluation loss**.
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- **training_loss = 0.9999**
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- **eval_loss = 1.0480**
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In general, a large gap between these two values may indicate overfitting.
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However, in this experiment, the difference was very small, which suggests that the model adapted to the training data in a stable manner while maintaining a similar level of performance on the validation set.
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In addition, the result of **eval_mean_token_accuracy = 0.7445** indicates that the model predicts the next token in a relatively stable and consistent way.
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Taken together, these results suggest that the model successfully learned the major patterns in the training data and converged stably without severe instability.
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## Overall Conclusion
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This fine-tuning was completed successfully and showed overall solid results.
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The close alignment between training loss and evaluation loss suggests that the model did not significantly overfit during training and that the optimization process remained stable.
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Moreover, no sharp divergence in loss values was observed throughout the training process.
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Therefore, this experiment can be regarded as a fine-tuning run that converged stably overall.
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## Limitations and Future Work
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While these metrics are useful for assessing training stability and convergence, they do not fully reflect response quality, factuality, instruction-following accuracy, or real-world usability.
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To obtain a more comprehensive evaluation of the model, the following additional assessments are planned:
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- evaluation on real user question-answer examples
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- downstream task performance evaluation
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- qualitative analysis of generated responses
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- safety and hallucination checks
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Through these follow-up evaluations, we aim to verify whether the model can go beyond stable training-time metrics and provide reliable and consistent performance in real-world usage scenarios.
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## License
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This model is a fine-tuned derivative of `LiquidAI/LFM2.5-1.2B-Instruct`.
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Use and distribution of this model are subject to the terms of the **LFM Open License v1.0** applicable to the base model.
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Please also review any additional obligations arising from the datasets used during fine-tuning.
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## Feedback
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This model will continue to be improved through further evaluation and refinement.
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If you have any feedback on your experience using the model or notice areas that need improvement, your input will be carefully considered and reflected in future quality improvements.
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Your feedback will be a great source of support in improving and further developing the model. |