Instructions to use Norquinal/PetrolLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Norquinal/PetrolLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Norquinal/PetrolLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norquinal/PetrolLM") model = AutoModelForCausalLM.from_pretrained("Norquinal/PetrolLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Norquinal/PetrolLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Norquinal/PetrolLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Norquinal/PetrolLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Norquinal/PetrolLM
- SGLang
How to use Norquinal/PetrolLM 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 "Norquinal/PetrolLM" \ --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": "Norquinal/PetrolLM", "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 "Norquinal/PetrolLM" \ --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": "Norquinal/PetrolLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Norquinal/PetrolLM with Docker Model Runner:
docker model run hf.co/Norquinal/PetrolLM
What is PetrolLM?
PetrolLM is Mistral-7B-v0.1 model fine-tune using QLoRA (4-bit precision) for the purposes of creative writing and roleplay.
The dataset consists of 5800 samples, with the composition as follows:
- AICG Logs (~17%)
- PygmalionAI/PIPPA (~17%)
- Squish42/bluemoon-fandom-1-1-rp-cleaned (~13%)
- OpenLeecher/Teatime (~2%)
- Norquinal/claude_multiround_chat_1k (~17%)
- jundurbin/airoboros-gpt4-1.4 (~17%)
- totally-not-an-llm/EverythingLM-data-V2-sharegpt (~17%)
These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
Prompt Format
The model was finetuned with a prompt format similar to the original SuperHOT prototype: ```
style: roleplay characters: [char]: [description] summary: [scenario]
Format: [char]: [message] Human: [message] ```Use in Text Generation Web UI
Install the bleeding-edge version of transformers from source:
pip install git+https://github.com/huggingface/transformers
Or, alternatively, change model_type in config.json from mistral to llama.
Use in SillyTavern UI
As an addendum, you can include one of the following as the Last Output Sequence:
Human: In your next reply, write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.
{{char}}:
{{char}} (2 paragraphs, engaging, natural, authentic, descriptive, creative):
[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
{{char}}:
The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs.
Finetuing Parameters
- LoRA Rank: 64
- LoRA Alpha: 16
- LoRA Dropout: 0.1
- BF16 Training
- Cutoff Length: 2048
- Training Epoch(s): 2
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