Instructions to use ToastyPigeon/ms3-roselily-rp-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToastyPigeon/ms3-roselily-rp-v2 with Transformers:

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

pipe = pipeline("text-generation", model="ToastyPigeon/ms3-roselily-rp-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ToastyPigeon/ms3-roselily-rp-v2")
model = AutoModelForCausalLM.from_pretrained("ToastyPigeon/ms3-roselily-rp-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ToastyPigeon/ms3-roselily-rp-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ToastyPigeon/ms3-roselily-rp-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ToastyPigeon/ms3-roselily-rp-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ToastyPigeon/ms3-roselily-rp-v2

SGLang

How to use ToastyPigeon/ms3-roselily-rp-v2 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 "ToastyPigeon/ms3-roselily-rp-v2" \
    --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": "ToastyPigeon/ms3-roselily-rp-v2",
		"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 "ToastyPigeon/ms3-roselily-rp-v2" \
        --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": "ToastyPigeon/ms3-roselily-rp-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ToastyPigeon/ms3-roselily-rp-v2 with Docker Model Runner:
```
docker model run hf.co/ToastyPigeon/ms3-roselily-rp-v2
```

ms3-roselily-rp-v2 / README.md

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metadata

base_model:
  - ToastyPigeon/ms3-roselily-instruct
  - ToastyPigeon/last-chance-ms3-s3-fix-qlora
library_name: transformers
tags:
  - mergekit
  - merge

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Passthrough merge method.

Models Merged

The following models were included in the merge:

ToastyPigeon/ms3-roselily-instruct + ToastyPigeon/last-chance-ms3-s3-fix-qlora

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ToastyPigeon/ms3-roselily-instruct+ToastyPigeon/last-chance-ms3-s3-fix-qlora
merge_method: passthrough