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.gitattributes

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README.md

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+---
+license: mit
+datasets:
+- cong1230/Mental_illness_chatbot_training_dataset
+language:
+- ko
+library_name: transformers
+pipeline_tag: text-generation
+description: 'Model Purpose and Target Domain:
+
+  This model is designed for text generation, specifically for the domain of mental
+  health counseling chatbots. Its aim is to provide support for various mental health
+  issues through conversations with users.
+
+
+  Unique Features and Capabilities:
+
+  The model specializes in mental health counseling, generating responses based on
+  users'' text inputs, performing sentiment analysis, and providing appropriate counseling.
+  It also incorporates knowledge about various mental health-related topics to offer
+  more effective counseling.
+
+
+  Performance Metrics and Benchmarks:
+
+  Specific information about performance metrics and benchmarks is not currently available.
+  The quantitative performance of the model needs to be evaluated in real-world usage
+  scenarios.
+
+
+  Training Procedure and Techniques:
+
+  The model was fine-tuned using the Peft library with Low-Rank Adaptation (LoRA)
+  technique. This approach allows the model to effectively learn and apply knowledge
+  and language specific to mental health counseling in chatbot interactions.'
+tags:
+- TensorBlock
+- GGUF
+base_model: cong1230/LDCC_LoRA_full
+---
+
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;">
+            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
+        </p>
+    </div>
+</div>
+
+## cong1230/LDCC_LoRA_full - GGUF
+
+This repo contains GGUF format model files for [cong1230/LDCC_LoRA_full](https://huggingface.co/cong1230/LDCC_LoRA_full).
+
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
+
+<div style="text-align: left; margin: 20px 0;">
+    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
+        Run them on the TensorBlock client using your local machine ↗
+    </a>
+</div>
+
+## Prompt template
+
+```
+
+```
+
+## Model file specification
+
+| Filename | Quant type | File Size | Description |
+| -------- | ---------- | --------- | ----------- |
+| [LDCC_LoRA_full-Q2_K.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q2_K.gguf) | Q2_K | 4.939 GB | smallest, significant quality loss - not recommended for most purposes |
+| [LDCC_LoRA_full-Q3_K_S.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q3_K_S.gguf) | Q3_K_S | 5.751 GB | very small, high quality loss |
+| [LDCC_LoRA_full-Q3_K_M.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q3_K_M.gguf) | Q3_K_M | 6.430 GB | very small, high quality loss |
+| [LDCC_LoRA_full-Q3_K_L.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q3_K_L.gguf) | Q3_K_L | 7.022 GB | small, substantial quality loss |
+| [LDCC_LoRA_full-Q4_0.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q4_0.gguf) | Q4_0 | 7.468 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [LDCC_LoRA_full-Q4_K_S.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q4_K_S.gguf) | Q4_K_S | 7.525 GB | small, greater quality loss |
+| [LDCC_LoRA_full-Q4_K_M.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q4_K_M.gguf) | Q4_K_M | 7.968 GB | medium, balanced quality - recommended |
+| [LDCC_LoRA_full-Q5_0.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q5_0.gguf) | Q5_0 | 9.083 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [LDCC_LoRA_full-Q5_K_S.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q5_K_S.gguf) | Q5_K_S | 9.083 GB | large, low quality loss - recommended |
+| [LDCC_LoRA_full-Q5_K_M.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q5_K_M.gguf) | Q5_K_M | 9.341 GB | large, very low quality loss - recommended |
+| [LDCC_LoRA_full-Q6_K.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q6_K.gguf) | Q6_K | 10.800 GB | very large, extremely low quality loss |
+| [LDCC_LoRA_full-Q8_0.gguf](https://huggingface.co/tensorblock/LDCC_LoRA_full-GGUF/blob/main/LDCC_LoRA_full-Q8_0.gguf) | Q8_0 | 13.988 GB | very large, extremely low quality loss - not recommended |
+
+
+## Downloading instruction
+
+### Command line
+
+Firstly, install Huggingface Client
+
+```shell
+pip install -U "huggingface_hub[cli]"
+```
+
+Then, downoad the individual model file the a local directory
+
+```shell
+huggingface-cli download tensorblock/LDCC_LoRA_full-GGUF --include "LDCC_LoRA_full-Q2_K.gguf" --local-dir MY_LOCAL_DIR
+```
+
+If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
+
+```shell
+huggingface-cli download tensorblock/LDCC_LoRA_full-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+```