---
base_model: Locutusque/gpt2-xl-conversational
datasets:
- Locutusque/InstructMix
language:
- en
license: mit
metrics:
- bleu
- perplexity
- loss
- accuracy
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
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
---

# antoste/gpt2-xl-conversational-Q4_K_M-GGUF
This model was converted to GGUF format from [`Locutusque/gpt2-xl-conversational`](https://huggingface.co/Locutusque/gpt2-xl-conversational) 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/Locutusque/gpt2-xl-conversational) 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 antoste/gpt2-xl-conversational-Q4_K_M-GGUF --hf-file gpt2-xl-conversational-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo antoste/gpt2-xl-conversational-Q4_K_M-GGUF --hf-file gpt2-xl-conversational-q4_k_m.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 antoste/gpt2-xl-conversational-Q4_K_M-GGUF --hf-file gpt2-xl-conversational-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo antoste/gpt2-xl-conversational-Q4_K_M-GGUF --hf-file gpt2-xl-conversational-q4_k_m.gguf -c 2048
```