Instructions to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Undi95/Llama-3-LewdPlay-8B-evo-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with Ollama:
```
ollama run hf.co/Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with Docker Model Runner:
```
docker model run hf.co/Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M
```

Lemonade

How to use Undi95/Llama-3-LewdPlay-8B-evo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Undi95/Llama-3-LewdPlay-8B-evo-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-LewdPlay-8B-evo-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Context size

by thedarktrumpet - opened May 11, 2024

Discussion

thedarktrumpet

May 11, 2024

One thing I had challenges with here was the context size. I wanted to give some of that information here incase anyone else runs into that same issue.

The standard context size for this model is 8k. If you're running this through text_gen_webui, you'll need to play with the compress_pos_emb or the alpha_value to scale it accordingly. The description is pretty good enough.

If you're using LocalAI instead, those features aren't available to you, but what is available is the rope_freq_base. The YAML I have for this is below:

context_size: 32768
f16: true
threads: 4
gpu_layers: 90
name: rp-llama3-lewdplay-8b
tensor_split: "90,0"
main_gpu: "0"
backend: llama-cpp
prompt_cache_all: false
parameters:
  model: Llama-3-LewdPlay-8B-evo.q8_0.gguf
  temperature: 0.6
  top_k: 40
  top_p: 0.95
  batch: 512
  tfz: 1.0
  n_keep: 0
  rope_freq_base:  8000000

The important one is the rope_freq_base, it's scaled up, and works well at 32k context size.

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