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

Libraries

How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-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 MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/Meta-Llama-3-8B-Instruct-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 MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/Meta-Llama-3-8B-Instruct-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 MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/Meta-Llama-3-8B-Instruct-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 MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

Ollama
How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

What am I missing? - Langchain vs LMStudio

#22

by jatesh007 - opened Apr 15, 2025

Discussion

jatesh007

Apr 15, 2025

Hi, I've just started playing around with Quantized LLMs and their capabilities and to be honest it is amazing! But when I try to programmatically infer something the result I get is not to par with LMStudio results.

For example :

1)
I have given a huge text from a Resume and asked the model the following questions. Summarize the resume in less than 100 words. What are the job roles to which this profile will be suitable to? What are the technologies in which they worked on? What is your profile rating for the below resume? - It answered great, though it was slow which is expected

I was expecting a similar response when I tried to infer the model with langchain using below code.

from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler

Callbacks support token-wise streaming

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

Make sure the model path is correct for your system!

llm = LlamaCpp(
model_path=MODEL_PATH,
callback_manager=callback_manager,
verbose=True, # Verbose is required to pass to the callback manager
n_ctx=4096,
n_threads=4,
n_batch = 512,
temperature=0.8,
top_p=0.95,
top_k=40,
repeat_penalty=1.1,
)

llm.invoke("""Resume Text Here""")

But it failed midway when it was trying to infer the content alone. It never reached to prediction of the answer.

When I tried with a resume text which is half of what I used initially, I was getting a response from the model now (through langchain) but the context was not up-to the mark.

I played around with prompts/prompt templates/Jinja2 Templates but did not have much luck.

I am looking for approach/guidance which I am missing here.

Long Story Short : What is it that I need to do extra to get a decent quality output that I get in LMStudio? Assuming I already have the default setting of the model whichever is available in LMStudio. A sample of how to infer the model, like the format of text/messages/content/instruction for better output.

Any little help would be appreciated. Thanks in Advance

jatesh007

Apr 15, 2025

@MaziyarPanahi Please help!

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