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Browse files- LICENSE +125 -0
- NOTICE +1 -0
- README.md +91 -0
- config.json +28 -0
- generation_config.json +6 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +298 -0
- special_tokens_map.json +33 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +87 -0
LICENSE
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LLAMA 2 COMMUNITY LICENSE AGREEMENT
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Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved
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README.md
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---
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license: apache-2.0
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datasets:
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- JetBrains/KExercises
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base_model: meta-llama/CodeLlama-7b-hf
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results:
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- task:
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type: text-generation
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dataset:
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name: MultiPL-HumanEval (Kotlin)
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type: openai_humaneval
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metrics:
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- name: pass@1
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type: pass@1
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value: 42.24
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tags:
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- code
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---
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# Kexer models
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Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset.
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This is a repository for the fine-tuned **CodeLlama-7b** model in the *Hugging Face Transformers* format.
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# How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load pre-trained model and tokenizer
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model_name = 'JetBrains/CodeLlama-7B-Kexer'
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
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# Create and encode input
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input_text = """\
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This function takes an integer n and returns factorial of a number:
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fun factorial(n: Int): Int {\
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"""
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input_ids = tokenizer.encode(
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input_text, return_tensors='pt'
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).to('cuda')
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# Generate
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output = model.generate(
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input_ids, max_length=60, num_return_sequences=1,
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early_stopping=True, pad_token_id=tokenizer.eos_token_id,
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)
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# Decode output
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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print(generated_text)
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```
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As with the base model, we can use FIM. To do this, the following format must be used:
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```
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'<PRE> ' + prefix + ' <SUF> ' + suffix + ' <MID>'
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```
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# Training setup
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The model was trained on one A100 GPU with the following hyperparameters:
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| **Hyperparameter** | **Value** |
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|:---------------------------:|:----------------------------------------:|
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| `warmup` | 10% |
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| `max_lr` | 1e-4 |
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| `scheduler` | linear |
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| `total_batch_size` | 256 (~130K tokens per step) |
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| `num_epochs` | 4 |
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More details about fine-tuning can be found in the technical report.
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# Fine-tuning data
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For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices dataset](https://huggingface.co/datasets/JetBrains/KExercises). Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens.
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# Evaluation
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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).
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Here are the results of our evaluation:
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| **Model name** | **Kotlin HumanEval Pass Rate** |
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|:---------------------------:|:----------------------------------------:|
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| `CodeLlama-7B` | 26.89 |
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| `CodeLlama-7B-Kexer` | **42.24** |
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# Ethical considerations and limitations
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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.
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config.json
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{
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"_name_or_path": "data/checkpoints/codel_kotlin_exercises_len-512_batch-64_lr-0.0001--id72475-124",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 16384,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.2",
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"use_cache": true,
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"vocab_size": 32016
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.39.2"
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}
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"β<EOT>",
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"β<EOT>"
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5 |
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12 |
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20 |
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22 |
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28 |
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29 |
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30 |
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36 |
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38 |
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39 |
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41 |
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42 |
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44 |
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46 |
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50 |
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52 |
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54 |
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"content": "β<EOT>",
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55 |
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58 |
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60 |
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61 |
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62 |
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63 |
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64 |
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65 |
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66 |
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"β<EOT>",
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67 |
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68 |
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69 |
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70 |
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"β<EOT>"
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71 |
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],
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72 |
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73 |
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74 |
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75 |
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"eot_token": "β<EOT>",
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84 |
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85 |
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86 |
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87 |
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}
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