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LICENSE

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+LLAMA 2 COMMUNITY LICENSE AGREEMENT	
+Llama 2 Version Release Date: July 18, 2023
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NOTICE

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

README.md

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+---
+license: apache-2.0
+datasets:
+- JetBrains/KStack-clean
+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: 37.89
+tags:
+- code
+---
+
+# Model description
+
+This is a repository for the **CodeLlama-7b** model fine-tuned on the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset with rule-based filtering, in the *Hugging Face Transformers* format. KStack-clean is a small subset of [KStack](https://huggingface.co/datasets/JetBrains/KStack), the largest collection of permissively licensed Kotlin code, automatically filtered to include files that have the highest "educational value for learning algorithms in Kotlin".
+
+# How to use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = 'JetBrains/CodeLlama-7B-KStack-clean'
+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, 
+    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: 
+```
+'<PRE> ' + prefix + ' <SUF> ' + suffix + ' <MID>'
+```
+
+# Training setup
+
+The model was trained on one A100 GPU with following hyperparameters:
+
+|         **Hyperparameter**           |             **Value**              |
+|:---------------------------:|:----------------------------------------:|
+|        `warmup`            |           100 steps            |
+|        `max_lr`        |          5e-5          |
+|        `scheduler`        |          linear          |
+|        `total_batch_size`        |        32 (~30K tokens per step)          |
+|        `num_epochs`        |          2          |
+
+More details about fine-tuning can be found in the technical report (coming soon!).
+
+# Fine-tuning data
+
+For tuning the model, we used 25K exmaples from the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset, selected from the larger [KStack](https://huggingface.co/datasets/JetBrains/KStack) dataset according to educational value for learning algorithms. In total, the dataset contains about 23M 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**              |
+|:---------------------------:|:----------------------------------------:|
+|           `CodeLlama-7B`            |           26.89            |
+|        `CodeLlama-7B-KStack-clean`        |          **37.89**        |
+
+# Ethical Considerations and Limitations
+
+CodeLlama-7B-KStack-clean 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-KStack-clean'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-KStack-clean, developers should perform safety testing and tuning tailored to their specific applications of the model.

config.json

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+{
+    "_name_or_path": "data/checkpoints/mshn_codel_bigcode_filt_len-512_batch-32_lr-5e-05_wup-100_topk-24000_seed-31--id22450-1500",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "max_position_embeddings": 16384,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 32,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.39.2",
+    "use_cache": true,
+    "vocab_size": 32016,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.21",
+        "bits": 2.5,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.2"
+}

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+    "▁<EOT>",
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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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