Instructions to use ChaoticNeutrals/RP_Vision_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/RP_Vision_7B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/RP_Vision_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/RP_Vision_7B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/RP_Vision_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/RP_Vision_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/RP_Vision_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/RP_Vision_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/RP_Vision_7B

SGLang

How to use ChaoticNeutrals/RP_Vision_7B 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 "ChaoticNeutrals/RP_Vision_7B" \
    --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": "ChaoticNeutrals/RP_Vision_7B",
		"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 "ChaoticNeutrals/RP_Vision_7B" \
        --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": "ChaoticNeutrals/RP_Vision_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChaoticNeutrals/RP_Vision_7B with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/RP_Vision_7B
```

Dataset

by Stark2008 - opened Jun 12, 2024

Discussion

Stark2008

Jun 12, 2024

Hey,

Can you say what dataset was used to create this model? Is the base model jeiku/Cookie_7B?

Thanks

jeiku

Jun 12, 2024

I honestly don't remember and I didn't save the config, so your guess is as good as mine.

WesPro

Jun 12, 2024

@stark2008 Depending on what you want to acchieve you could use mergekit and the base model to extract the finetuning data into a lora adapter.

jeiku

Jun 12, 2024

in that vein, the base model would have been Mistral 0.1 definitely, though I can't confidently say whether it was instruct or base as it was the product of merges

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment