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Model: JetBrains/deepseek-coder-6.7B-kexer
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- JetBrains/KExercises
+base_model: deepseek-ai/deepseek-coder-6.7b-base
+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
+---
+
+# 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
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = 'JetBrains/deepseek-coder-6.7B-kexer'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
+
+# Create and encode input
+input_text = """\
+This function takes an integer n and returns factorial of a number:
+fun factorial(n: Int): Int {\
+"""
+input_ids = tokenizer.encode(
+    input_text, return_tensors='pt'
+).to('cuda')
+
+# Generate
+output = model.generate(
+    input_ids, max_length=60, num_return_sequences=1, 
+    early_stopping=True, pad_token_id=tokenizer.eos_token_id,
+)
+
+# Decode output
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+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.