Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- JetBrains/KExercises
+base_model: JetBrains/deepseek-coder-6.7B-kexer
+results:
+- task:
+    type: text-generation
+  dataset:
+    name: MultiPL-HumanEval (Kotlin)
+    type: openai_humaneval
+  metrics:
+  - name: pass@1
+    type: pass@1
+    value: 55.28
+tags:
+- code
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Deepseek-Coder-6.7B-kexer-GGUF
+This is quantized version of [JetBrains/deepseek-coder-6.7B-kexer](https://huggingface.co/JetBrains/deepseek-coder-6.7B-kexer) created using llama.cpp
+
+# Kexer models
+
+Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. 
+This is a repository for the fine-tuned **Deepseek-coder-6.7b** model in the *Hugging Face Transformers* format. 
+
+# How to use
+
+As with the base model, we can use FIM. To do this, the following format must be used: 
+```
+'<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>'
+```
+
+# Training setup
+
+The model was trained on one A100 GPU with following hyperparameters:
+
+|         **Hyperparameter**           |             **Value**              |
+|:---------------------------:|:----------------------------------------:|
+|           `warmup`            |           10%            |
+|        `max_lr`        |          1e-4          |
+|        `scheduler`        |          linear          |
+|        `total_batch_size`        |          256 (~130K tokens per step)          |
+|        `num_epochs`        |          4          |
+
+More details about fine-tuning can be found in the technical report (coming soon!).
+
+# Fine-tuning data
+
+For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens. 
+
+# Evaluation 
+
+For evaluation, we used the [Kotlin HumanEval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval) dataset, which contains all 161 tasks from HumanEval translated into Kotlin by human experts. You can find more details about the pre-processing necessary to obtain our results, including the code for running, on the [datasets's page](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval).
+
+Here are the results of our evaluation:
+
+|         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
+|:---------------------------:|:----------------------------------------:|
+|           `Deepseek-coder-6.7B`            |           40.99            |
+|        `Deepseek-coder-6.7B-kexer`        |          **55.28**         |
+
+# Ethical considerations and limitations
+
+Deepseek-coder-6.7B-kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Deepseek-coder-6.7B-kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Deepseek-coder-6.7B-kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.