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LDCC_LoRA_full-GGUF

Text Generation
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GGUF
Korean
TensorBlock
GGUF
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LDCC_LoRA_full-GGUF / README.md
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metadata
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.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Prompt template

Model file specification

Filename Quant type File Size Description
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 Q3_K_S 5.751 GB very small, high quality loss
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 Q3_K_L 7.022 GB small, substantial quality loss
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 Q4_K_S 7.525 GB small, greater quality loss
LDCC_LoRA_full-Q4_K_M.gguf Q4_K_M 7.968 GB medium, balanced quality - recommended
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 Q5_K_S 9.083 GB large, low quality loss - recommended
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 Q6_K 10.800 GB very large, extremely low quality loss
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

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

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:

huggingface-cli download tensorblock/LDCC_LoRA_full-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'