Instructions to use grimjim/llama-3-experiment-v1-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/llama-3-experiment-v1-9B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/llama-3-experiment-v1-9B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/llama-3-experiment-v1-9B-GGUF")
model = AutoModelForCausalLM.from_pretrained("grimjim/llama-3-experiment-v1-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-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-experiment-v1-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M

SGLang

How to use grimjim/llama-3-experiment-v1-9B-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-experiment-v1-9B-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-experiment-v1-9B-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-experiment-v1-9B-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-experiment-v1-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use grimjim/llama-3-experiment-v1-9B-GGUF with Ollama:
```
ollama run hf.co/grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use grimjim/llama-3-experiment-v1-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/llama-3-experiment-v1-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/llama-3-experiment-v1-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-experiment-v1-9B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

llama-3-experiment-v1-9B-GGUF

This is an experimental merge, replicating additional layers to the model without post-merge healing. There is damage to the model, but it appears to be tolerable as is. The resulting impact on narrative text completion may be of interest.

Light testing performed with instruct prompting and the following sampler settings:

temp=1 and minP=0.02
temp=1 and smoothing factor=0.33

Full weights: grimjim/llama-3-experiment-v1-9B

GGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF

This is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.

Built with Meta Llama 3.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

meta-llama/Meta-Llama-3-8B-Instruct

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: meta-llama/Meta-Llama-3-8B-Instruct
      layer_range: [0, 12]
  - sources:
    - model: meta-llama/Meta-Llama-3-8B-Instruct
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16