Instructions to use grimjim/llama-3-Nephilim-v3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="grimjim/llama-3-Nephilim-v3-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/llama-3-Nephilim-v3-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/llama-3-Nephilim-v3-8B-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 grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/llama-3-Nephilim-v3-8B-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 grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/llama-3-Nephilim-v3-8B-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 grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/llama-3-Nephilim-v3-8B-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 grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/llama-3-Nephilim-v3-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/llama-3-Nephilim-v3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M

SGLang

How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "grimjim/llama-3-Nephilim-v3-8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/llama-3-Nephilim-v3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "grimjim/llama-3-Nephilim-v3-8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/llama-3-Nephilim-v3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with Ollama:
```
ollama run hf.co/grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for grimjim/llama-3-Nephilim-v3-8B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for grimjim/llama-3-Nephilim-v3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grimjim/llama-3-Nephilim-v3-8B-GGUF to start chatting

Docker Model Runner
How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/llama-3-Nephilim-v3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/llama-3-Nephilim-v3-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-Nephilim-v3-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

llama-3-Nephilim-v3-8B-GGUF

This repo contains select GGUF quants of a merge of pre-trained language models created using mergekit.

Full weights are here.

Although none of the components of this merge were trained for roleplay nor intended for it, the model can be used effectively in that role.

Tested with temperature 1 and minP 0.01. This model leans toward being creative, so adjust temperature upward or downward as desired.

There are initial format consistency issues with the merged model, but this can be mitigated in an Instruct prompt. Additionally, promptsteering was employed to vary the text generation output to avoid some of the common failings observed during text generation with Llama 3 8B models. The complete Instruct prompt used during testing is available below.

Built with Meta Llama 3.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge as a base.

Models Merged

The following models were included in the merge:

tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1

Configuration

The following YAML configuration was used to produce this model:

base_model: grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge
  - layer_range: [0, 32]
    model: tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1
    parameters:
      weight: 0.1