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

Libraries

How to use impactframes/IF_PromptMKR_GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="impactframes/IF_PromptMKR_GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("impactframes/IF_PromptMKR_GPTQ")
model = AutoModelForCausalLM.from_pretrained("impactframes/IF_PromptMKR_GPTQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use impactframes/IF_PromptMKR_GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "impactframes/IF_PromptMKR_GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impactframes/IF_PromptMKR_GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/impactframes/IF_PromptMKR_GPTQ

SGLang

How to use impactframes/IF_PromptMKR_GPTQ 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/IF_PromptMKR_GPTQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impactframes/IF_PromptMKR_GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/IF_PromptMKR_GPTQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impactframes/IF_PromptMKR_GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

IF_PromptMKR_GPTQ

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---

license: llama2
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
This Model is meant to be use with my extesion for Automatic1111 and SDNext 
Is a Qlora base on about 80K Alpaca style instructions base on top of LlaMa2 13B instruct 
https://github.com/if-ai/IF_prompt_MKR

 -`♡´- Thanks to all my supporters on Youtube and kofi @impactframes 

[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/O4O51R44U)

[![YouTube Video](https://img.youtube.com/vi/dg_8cGzzfY4/0.jpg)](https://youtu.be/dg_8cGzzfY4) 
[![YouTube Video](https://img.youtube.com/vi/Y1E_y7ZrX5w/0.jpg)](https://youtu.be/Y1E_y7ZrX5w)
[![
YouTube Video](https://img.youtube.com/vi/Bg9jV2Vxkk4/0.jpg)](https://youtu.be/Bg9jV2Vxkk4)