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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`](https://huggingface.co/Arjun-G-Ravi/chat-GPT2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Arjun-G-Ravi/chat-GPT2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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"
```
or
```
./llama-server --hf-repo Sansar-Karki/chat-GPT2-Q8_0-GGUF --hf-file chat-gpt2-q8_0.gguf -c 2048
```
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