---
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
---

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## 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>
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## 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'
```