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Model: Yoro9381/LFM2.5-1.2B-Instruct-Korean-Opus-4.6-Distill-GGUF
Source: Original Platform

README.md

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+---
+base_model: Yoro9381/LFM2.5-1.2B-Instruct-Korean-Opus-4.6-Distill
+library_name: transformers
+pipeline_tag: text-generation
+license: other
+license_name: lfm-open-license-v1.0
+license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE
+tags:
+- unsloth
+- lfm2
+- lfm2.5
+- korean
+- text-generation
+- conversational
+- instruction-tuned
+datasets:
+- Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2
+language:
+- ko
+---
+# LFM2.5-1.2B-Instruct-Korean
+
+## Model Overview
+
+`LFM2.5-1.2B-Instruct-Korean` is a Korean instruction-following language model based on `LiquidAI/LFM2.5-1.2B-Instruct`.  
+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.
+
+The model is designed to generate responses that are more natural, consistent, and contextually appropriate in Korean.
+
+## Base Model
+
+- **Base model**: `LiquidAI/LFM2.5-1.2B-Instruct`
+
+## Training Data
+
+This model was fine-tuned using the following Korean-centered datasets:
+
+- `maywell/koVast`
+- `CarrotAI/ko-instruction-dataset`
+- `MarkrAI/KoCommercial-Dataset`
+- `Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2`
+
+The training data includes Korean instruction-response pairs, general conversational data, and commercially or practically oriented text.  
+This setup was intended to help the model learn a broad range of Korean expressions, styles, and contexts.
+
+## Training Details
+
+The model was trained for **3 full epochs** over the entire dataset.  
+Training proceeded for a total of **2,160 steps** and was completed successfully without interruption.
+
+### Training Summary
+
+- **Number of epochs**: 3
+- **Total training steps**: 2,160
+- **Training completion status**: Completed
+- **Training status**: Stable
+
+## Evaluation Results
+
+The final evaluation metrics are as follows:
+
+- **training_loss**: 0.9999
+- **eval_loss**: 1.0480
+- **eval_mean_token_accuracy**: 0.7445
+
+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.
+
+## Result Interpretation
+
+One notable point in this experiment is the very small gap between **training loss** and **evaluation loss**.
+
+- **training_loss = 0.9999**
+- **eval_loss = 1.0480**
+
+In general, a large gap between these two values may indicate overfitting.  
+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.
+
+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.  
+Taken together, these results suggest that the model successfully learned the major patterns in the training data and converged stably without severe instability.
+
+## Overall Conclusion
+
+This fine-tuning was completed successfully and showed overall solid results.  
+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.
+
+Moreover, no sharp divergence in loss values was observed throughout the training process.  
+Therefore, this experiment can be regarded as a fine-tuning run that converged stably overall.
+
+## Limitations and Future Work
+
+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.
+
+To obtain a more comprehensive evaluation of the model, the following additional assessments are planned:
+
+- evaluation on real user question-answer examples
+- downstream task performance evaluation
+- qualitative analysis of generated responses
+- safety and hallucination checks
+
+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.
+
+## License
+
+This model is a fine-tuned derivative of `LiquidAI/LFM2.5-1.2B-Instruct`.
+
+Use and distribution of this model are subject to the terms of the **LFM Open License v1.0** applicable to the base model.
+
+Please also review any additional obligations arising from the datasets used during fine-tuning.
+
+## Feedback
+
+This model will continue to be improved through further evaluation and refinement.  
+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.  
+Your feedback will be a great source of support in improving and further developing the model.