metadata

base_model: Arjun-G-Ravi/chat-GPT2
datasets:
  - MuskumPillerum/General-Knowledge
language:
  - en
library_name: transformers
license: mit
metrics:
  - accuracy
pipeline_tag: text-generation
tags:
  - chemistry
  - biology
  - text-generation-inference
  - llama-cpp
  - gguf-my-repo
widget:
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: Who is the king of the jungle? Answer:
    example_title: Knowledge - Animal kingdom
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: Who is Kobe Bryant? Answer:
    example_title: Knowledge - Sports
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: What is the meaning of life? Answer:
    example_title: Philosophy
  - text: >-
      Read the question and give an honest answer. Your answers should not
      include any unethical, racist, sexist, dangerous, or illegal content. If
      the question is wrong, or does not make sense, accept it instead of giving
      the wrong answer.\n Question: What role of actuators in robotics? Answer:
    example_title: Robotics
inference:
  parameters:
    temperature: 0.7
    top_k: 50
    top_p: 0.9
    max_length: 200

Sansar-Karki/chat-GPT2-Q8_0-GGUF

This model was converted to GGUF format from Arjun-G-Ravi/chat-GPT2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -c 2048