Upload deepseek-coder-6.7B-kexer.Q2_K.gguf with huggingface_hub

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 ---
-license: Apache License 2.0
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
+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
 ---
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型

-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
+# 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: 
 ```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('QuantFactory/deepseek-coder-6.7B-kexer-GGUF')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/QuantFactory/deepseek-coder-6.7B-kexer-GGUF.git
+'<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>'
 ```

-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+# 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.