Instructions to use tavtav/Rose-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tavtav/Rose-20B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tavtav/Rose-20B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tavtav/Rose-20B") model = AutoModelForCausalLM.from_pretrained("tavtav/Rose-20B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tavtav/Rose-20B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tavtav/Rose-20B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tavtav/Rose-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tavtav/Rose-20B
- SGLang
How to use tavtav/Rose-20B 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 "tavtav/Rose-20B" \ --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": "tavtav/Rose-20B", "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 "tavtav/Rose-20B" \ --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": "tavtav/Rose-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tavtav/Rose-20B with Docker Model Runner:
docker model run hf.co/tavtav/Rose-20B
Rose-20B

Experimental Frankenmerge Model
Other Formats
Model Details
A Frankenmerge with Thorns-13B by CalderaAI and Noromaid-13-v0.1.1 by NeverSleep (IkariDev and Undi). This recipe was proposed by Trappu and the layer distribution recipe was made by Undi. I thank them for sharing their knowledge with me. This model should be very good at any roleplay scenarios. I called the model "Rose" because it was a fitting name for a "thorny maid".
The recommended format to use is Alpaca.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
Feel free to share any other prompts that works. This model is very robust.
Warning: This model uses significantly more VRAM due to the KV cache increase resulting in more VRAM required for the context window.
Justification for its Existence
Potential base model for finetune experiments using our dataset to create Pygmalion-20B. Due to the already high capabilities, adding our dataset will mesh well with how the model performs. Potential experimentation with merging with other 20B Frankenmerge models.
Model Recipe
slices:
- sources:
- model: Thorns-13B
layer_range: [0, 16]
- sources:
- model: Noromaid-13B
layer_range: [8, 24]
- sources:
- model: Thorns-13B
layer_range: [17, 32]
- sources:
- model: Noromaid-13B
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
Again, credits to Undi for the recipe.
Reception
The model was given to a handful of members in the PygmalionAI Discord community for testing. A strong majority really enjoyed the model with only a couple giving the model a passing grade. Since our community has high standards for roleplaying models, I was surprised at the positive reception.
Contact
Send a message to tav (tav) on Discord if you want to talk about the model to me. I'm always open to receive comments.
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