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gpt2-xl-conversational-GGUF

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metadata
license: mit
datasets:
  - Locutusque/InstructMix
language:
  - en
metrics:
  - bleu
  - perplexity
  - loss
  - accuracy
pipeline_tag: text-generation
widget:
  - text: >-
      <|USER|> Design a Neo4j database and Cypher function snippet to Display
      Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners.
      Implement if/else or switch/case statements to handle different conditions
      related to the Consent. Provide detailed comments explaining your control
      flow and the reasoning behind each decision. <|ASSISTANT|> 
  - text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
  - text: >-
      <|USER|> Write me an essay about the life of George Washington
      <|ASSISTANT|> 
  - text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
  - text: >-
      <|USER|> Craft me a list of some nice places to visit around the world.
      <|ASSISTANT|> 
  - text: >-
      <|USER|> How to manage a lazy employee: Address the employee verbally.
      Don't allow an employee's laziness or lack of enthusiasm to become a
      recurring issue. Tell the employee you're hoping to speak with them about
      workplace expectations and performance, and schedule a time to sit down
      together. Question: To manage a lazy employee, it is suggested to talk to
      the employee. True, False, or Neither? <|ASSISTANT|> 
inference:
  parameters:
    temperature: 0.8
    do_sample: true
    top_p: 0.14
    top_k: 41
    max_new_tokens: 250
    repetition_penalty: 1.176
base_model: Locutusque/gpt2-xl-conversational
tags:
  - TensorBlock
  - GGUF
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Locutusque/gpt2-xl-conversational - GGUF

This repo contains GGUF format model files for Locutusque/gpt2-xl-conversational.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template

Model file specification

Filename Quant type File Size Description
gpt2-xl-conversational-Q2_K.gguf Q2_K 0.845 GB smallest, significant quality loss - not recommended for most purposes
gpt2-xl-conversational-Q3_K_S.gguf Q3_K_S 0.845 GB very small, high quality loss
gpt2-xl-conversational-Q3_K_M.gguf Q3_K_M 0.966 GB very small, high quality loss
gpt2-xl-conversational-Q3_K_L.gguf Q3_K_L 1.027 GB small, substantial quality loss
gpt2-xl-conversational-Q4_0.gguf Q4_0 0.906 GB legacy; small, very high quality loss - prefer using Q3_K_M
gpt2-xl-conversational-Q4_K_S.gguf Q4_K_S 1.037 GB small, greater quality loss
gpt2-xl-conversational-Q4_K_M.gguf Q4_K_M 1.110 GB medium, balanced quality - recommended
gpt2-xl-conversational-Q5_0.gguf Q5_0 1.087 GB legacy; medium, balanced quality - prefer using Q4_K_M
gpt2-xl-conversational-Q5_K_S.gguf Q5_K_S 1.149 GB large, low quality loss - recommended
gpt2-xl-conversational-Q5_K_M.gguf Q5_K_M 1.286 GB large, very low quality loss - recommended
gpt2-xl-conversational-Q6_K.gguf Q6_K 1.519 GB very large, extremely low quality loss
gpt2-xl-conversational-Q8_0.gguf Q8_0 1.630 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/gpt2-xl-conversational-GGUF --include "gpt2-xl-conversational-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/gpt2-xl-conversational-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'