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

Libraries

How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Llama-3-8B-Instruct-Coder-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama-3-8B-Instruct-Coder-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-8B-Instruct-Coder-GGUF",
	filename="Llama-3-8B-Instruct-Coder.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-8B-Instruct-Coder-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 QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-8B-Instruct-Coder-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 QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-8B-Instruct-Coder-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": "QuantFactory/Llama-3-8B-Instruct-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Llama-3-8B-Instruct-Coder-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 "QuantFactory/Llama-3-8B-Instruct-Coder-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": "QuantFactory/Llama-3-8B-Instruct-Coder-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 "QuantFactory/Llama-3-8B-Instruct-Coder-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": "QuantFactory/Llama-3-8B-Instruct-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama-3-8B-Instruct-Coder-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 QuantFactory/Llama-3-8B-Instruct-Coder-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 QuantFactory/Llama-3-8B-Instruct-Coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama-3-8B-Instruct-Coder-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-8B-Instruct-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-8B-Instruct-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-Instruct-Coder-GGUF-Q4_K_M

List all available models

lemonade list

@@ -33,4 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-Llama-3-8B-Instruct-Coder.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q4_1.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q5_1.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3-8B-Instruct-Coder.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text

QuantFactory
/

Llama-3-8B-Instruct-Coder-GGUF

Upload folder using huggingface_hub

+version https://git-lfs.github.com/spec/v1
+oid sha256:122238b186a51b7c55314d3bda6b63971c80c2c31b5b657f3088e912dfbbb4ab
+size 3179644224

+version https://git-lfs.github.com/spec/v1
+oid sha256:de6d4f90d6f28894bb336c75e312c562d24b59c190c1c51a1a1618c6d822349d
+size 4322469184

+version https://git-lfs.github.com/spec/v1
+oid sha256:7a19581890d556c2dc5f8c69e3ba1c4b8d3a9e398b10ac87ad25dce38ebec6c3
+size 4019430720

+version https://git-lfs.github.com/spec/v1
+oid sha256:11c62ad782fd07e5e14ceb0ac1cc1c7a0d2b62a6b98a221832300098f67d4c0f
+size 3665012032

+version https://git-lfs.github.com/spec/v1
+oid sha256:eb9e333aa929a7d03de42d540c31dc5af4218c50a858557253532f84d661bded
+size 4661724480

+version https://git-lfs.github.com/spec/v1
+oid sha256:ed0ced3c77acbc644b516f42dbc7e46cfdc5f68749d56a07947161bf7115c5b3
+size 5130765632

+version https://git-lfs.github.com/spec/v1
+oid sha256:1719c0fc7793d5f093185d1a1f78d3e11e441d8e81fec6892f9ae4a7f5d1872a
+size 4921247040

+version https://git-lfs.github.com/spec/v1
+oid sha256:a23ae619c1d4b0bfda430b4273a91455192178da70b8f76122f3d8831fbb0c6a
+size 4693181760

+version https://git-lfs.github.com/spec/v1
+oid sha256:391760d24cb3ce1664dab9d6ecf487e2f2b792adc9f0cd3b9c6a39db9b55afb4
+size 5599806784

+version https://git-lfs.github.com/spec/v1
+oid sha256:8b08be084ec1e1c6f4423c64ae7f2a5cbcf1afd7b492971dc7e5bc64cf32befa
+size 6068847936

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:b2406ebd53bcbbe3f5c4a73d9a50df85e0f5d8546bff37259b1493caea1994eb
+size 5733500224

+version https://git-lfs.github.com/spec/v1
+oid sha256:6cd85a2dc8d457f0a8fbc0719f2df0847a10556baabb454a55b7a28151d5410f
+size 6596519232