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LICENSE

@@ -1,125 +0,0 @@
-LLAMA 2 COMMUNITY LICENSE AGREEMENT	
-Llama 2 Version Release Date: July 18, 2023
-
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-"Llama 2" means the foundational large language models and software and 
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NOTICE

@@ -1 +0,0 @@
-Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved

README.md

@@ -2,27 +2,26 @@
 license: apache-2.0
 datasets:
 - JetBrains/KExercises
-base_model: meta-llama/CodeLlama-7b-hf
 results:
-- task:
-    type: text-generation
-  dataset:
-    name: MultiPL-HumanEval (Kotlin)
-    type: openai_humaneval
-  metrics:
-  - name: pass@1
-    type: pass@1
-    value: 42.24
+  - task:
+      type: text-generation
+    dataset:
+      name: MultiPL-HumanEval (Kotlin)
+      type: openai_humaneval
+    metrics:
+      - name: pass@1
+        type: pass@1
+        value: 42.24
 tags:
 - code
 ---
 
 # 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 **CodeLlama-7b** model in the *Hugging Face Transformers* format.
+Kexer models is a collection of fine-tuned open-source generative text models fine-tuned on Kotlin Exercices dataset. 
+This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
 
-# How to use
+# Model use
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -43,8 +42,8 @@ input_ids = tokenizer.encode(
 
 # Generate
 output = model.generate(
-    input_ids, max_length=60, num_return_sequences=1, 
-    early_stopping=True, pad_token_id=tokenizer.eos_token_id,
+    input_ids, max_length=150, num_return_sequences=1, 
+    no_repeat_ngram_size=2, early_stopping=True
 )
 
 # Decode output
@@ -59,7 +58,7 @@ As with the base model, we can use FIM. To do this, the following format must be
 
 # Training setup
 
-The model was trained on one A100 GPU with the following hyperparameters:
+The model was trained on one A100 GPU with following hyperparameters:
 
 |         **Hyperparameter**           |             **Value**              |
 |:---------------------------:|:----------------------------------------:|
@@ -69,23 +68,24 @@ The model was trained on one A100 GPU with the following hyperparameters:
 |        `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!).
+More details about finetuning can be found in the technical report
 
 # 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. 
+For this model we used 15K exmaples of Kotlin Exercices dataset {TODO: link!}. Every example follows HumanEval like format. In total dataset contains about 3.5M tokens. 
+For more information about the dataset follow the link.
 
 # 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).
+To evaluate we used Kotlin Humaneval (more infromation here)
 
-Here are the results of our evaluation:
+Fine-tuned model:
 
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
-|           `CodeLlama-7B`            |           26.89            |
-|        `CodeLlama-7B-Kexer`        |          **42.24**         |
+|           `base model`            |           26.89            |
+|        `fine-tuned model`        |          42.24         |
 
-# Ethical considerations and limitations
+# Ethical Considerations and Limitations
 
-CodeLlama-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, CodeLlama-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 CodeLlama-7B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
+Code Llama and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, 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 Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.