Instructions to use impactframes/llama3_IF_AI_SDPromptMkr_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use impactframes/llama3_IF_AI_SDPromptMkr_16bit with Transformers:

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

pipe = pipeline("text-generation", model="impactframes/llama3_IF_AI_SDPromptMkr_16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("impactframes/llama3_IF_AI_SDPromptMkr_16bit")
model = AutoModelForCausalLM.from_pretrained("impactframes/llama3_IF_AI_SDPromptMkr_16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use impactframes/llama3_IF_AI_SDPromptMkr_16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "impactframes/llama3_IF_AI_SDPromptMkr_16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impactframes/llama3_IF_AI_SDPromptMkr_16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/impactframes/llama3_IF_AI_SDPromptMkr_16bit

SGLang

How to use impactframes/llama3_IF_AI_SDPromptMkr_16bit 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 "impactframes/llama3_IF_AI_SDPromptMkr_16bit" \
    --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": "impactframes/llama3_IF_AI_SDPromptMkr_16bit",
		"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 "impactframes/llama3_IF_AI_SDPromptMkr_16bit" \
        --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": "impactframes/llama3_IF_AI_SDPromptMkr_16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use impactframes/llama3_IF_AI_SDPromptMkr_16bit 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 impactframes/llama3_IF_AI_SDPromptMkr_16bit 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 impactframes/llama3_IF_AI_SDPromptMkr_16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for impactframes/llama3_IF_AI_SDPromptMkr_16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="impactframes/llama3_IF_AI_SDPromptMkr_16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use impactframes/llama3_IF_AI_SDPromptMkr_16bit with Docker Model Runner:
```
docker model run hf.co/impactframes/llama3_IF_AI_SDPromptMkr_16bit
```

LLaMA3 License and Usage

Introduction

The LLaMA3 model is equipped to deliver superior results in machine learning applications. This model is particularly effective when used in conjunction with the IF_AI_tools custom node for ComfyUI and the IF_PromptMKr, my extension for A1111 Forge and Next platforms.

Model Training

LLaMA3 has been meticulously trained on a synthetic dataset comprising over 50,000 high-quality, stable diffusion prompts, ensuring robustness and high performance across various tasks.

Useful Links

Support

Your support is invaluable in continuing the development and enhancement of tools like these. If you find this tool useful, please consider extending your support by:

Starring the repository on GitHub: Star ComfyUI-IF_AI_tools
Subscribing to my YouTube channel: Impact Frames on YouTube
Donating on Ko-fi: Support Impact Frames on Ko-fi
Becoming a patron on Patreon: Support via Patreon

Thank you for your interest and support!

Developed by: impactframes
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

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