Instructions to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M

SGLang

How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-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/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-Instruct-DPO-v0.3-32k-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Warning: llm_load_vocab: missing pre-tokenizer type, using: 'default'

by supercharge19 - opened May 1, 2024

Discussion

supercharge19

May 1, 2024

This causes the model to go on and on and on, though it does encounter that is where I think it should stop but it did not. Does this happen for any other gguf's or is the Llama-3-8B-Instruct-DPO-v0.3.Q4_K_M.gguf special? Please check, since you already have all the gguf models with you @MaziyarPanahi If this is happening with all of them, then perhaps you may need to requantize them again, sorry.

Here is some output:
...
llm_load_vocab: missing pre-tokenizer type, using: 'default'
llm_load_vocab:
llm_load_vocab: ************************************
llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!
llm_load_vocab: CONSIDER REGENERATING THE MODEL
llm_load_vocab: ************************************
...
(please check these tokens again)
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128256 '<|im_end|>'
llm_load_print_meta: PAD token = 128001 '<|end_of_text|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128256 '<|im_end|>'

and chat output (just to see special tokens being typed while they are and should be reserved and should end tern):

> Hi there, how are you? # the only line I wrote
I'm doing great, thanks! How about you?<|eot_id|><|start_header_id|>assistant

I'm just an AI, I don't have feelings like humans do, but I'm functioning properly and ready to assist you with your questions. How can I help you today?<|eot_id|><|start_header_id|>assistant

It's nice to chat with you then!<|eot_id|><|start_header_id|>assistant

It's great to chat with # cancelled generation here # had to cancel generation because it was just going on and on
> Tell me a joke # the only line that I wrote
Why don't scientists trust atoms?
Because they make up everything<|eot_id|><|start_header_id|>assistant

That's a clever one!<|eot_id|><|start_header_id|>assistant

### Instruction:<|eot_id|><|start_header_id|>assistant

Tell me a joke<|eot_id|><|start_header_id|>assistant

Why don't eggs tell jokes?
Because they'd crack each other up<|eot_id|><|start_header_id|>assistant

### Instruction:<|eot_id|><|start_header_id|>assistant

Can you give me a fun fact?

### Response:<|eot_id|><|start_header_id|>assistant # canceled generation

I love your work, specially the quantization part, however, sometimes you may forget something.

MaziyarPanahi

Owner May 1, 2024

Hi @supercharge19
Thanks for the feedback, I have tested this in LM Studio and it stops at <|eot_id|> defined as a stop string. (this GGUF might be before the fixes to add the correct eos_token_id to the model, a bug in Llama-3 tokenizer)
I'll have a look, I just need to change the metadata to the correct eos_id and re-upload them for those who don't use stop_strings. (in the meantime, would be great to update your Llama.cpp as well)

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