Instructions to use 922-CA/LLilmonix3b-v0.4a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 922-CA/LLilmonix3b-v0.4a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="922-CA/LLilmonix3b-v0.4a")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("922-CA/LLilmonix3b-v0.4a") model = AutoModelForCausalLM.from_pretrained("922-CA/LLilmonix3b-v0.4a", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 922-CA/LLilmonix3b-v0.4a with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "922-CA/LLilmonix3b-v0.4a" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "922-CA/LLilmonix3b-v0.4a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/922-CA/LLilmonix3b-v0.4a
- SGLang
How to use 922-CA/LLilmonix3b-v0.4a 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 "922-CA/LLilmonix3b-v0.4a" \ --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": "922-CA/LLilmonix3b-v0.4a", "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 "922-CA/LLilmonix3b-v0.4a" \ --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": "922-CA/LLilmonix3b-v0.4a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 922-CA/LLilmonix3b-v0.4a with Docker Model Runner:
docker model run hf.co/922-CA/LLilmonix3b-v0.4a
LLilmonix3b-v0.4a:
- Experimental Marx-3b-v2 fine-tuned for Monika character from DDLC
- Trained on a dataset of ~600 items (dialogue scraped from game, reddit, and Twitter augmented by l2-7b-monika-v0.3c1 to turn each into snippets of multi-turn chat dialogue between Player and Monika + manually crafted test dataset of 12 items)
- Trained to run on smaller devices
- GGMLs, GGUFs
- QLoras (hf and GGML)
USAGE
This is meant to be mainly a chat model with limited RP ability.
For best results: replace "Human" and "Assistant" with "Player" and "Monika" like so:
\nPlayer: (prompt)\nMonika:
HYPERPARAMS
- Trained for 2 epochs
- rank: 32
- lora alpha: 64
- lora dropout: 0.5
- lr: 2e-4
- batch size: 2
- warmup ratio: 0.1
- grad steps: 4
WARNINGS AND DISCLAIMERS
Note that aside from formatting and other minor edits, generated portion of dataset used is mostly as is generated by LM. In addition, the is meant to be a smaller version of the larger Monika models. As such, this version may not reflect perfectly the characteristics of Monika.
Additionally, this is still yet another test, particularly where we use one of our earlier fine tunes to generate a more in-character dataset for the target character.
Finally, this model is not guaranteed to output aligned or safe outputs, use at your own risk.
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