Instructions to use lemonilia/LimaRP-perscengen-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemonilia/LimaRP-perscengen-v5 with Transformers:

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

pipe = pipeline("text-generation", model="lemonilia/LimaRP-perscengen-v5", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("lemonilia/LimaRP-perscengen-v5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("lemonilia/LimaRP-perscengen-v5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lemonilia/LimaRP-perscengen-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemonilia/LimaRP-perscengen-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonilia/LimaRP-perscengen-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lemonilia/LimaRP-perscengen-v5

SGLang

How to use lemonilia/LimaRP-perscengen-v5 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 "lemonilia/LimaRP-perscengen-v5" \
    --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": "lemonilia/LimaRP-perscengen-v5",
		"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 "lemonilia/LimaRP-perscengen-v5" \
        --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": "lemonilia/LimaRP-perscengen-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lemonilia/LimaRP-perscengen-v5 with Docker Model Runner:
```
docker model run hf.co/lemonilia/LimaRP-perscengen-v5
```

LimaRP Persona-Scenario Generator (v5, Alpaca)

A previously unpublished LoRA adapter for Yarn-Llama-2-7B-64k made for internal use. Its primary purpose is generating Persona and Scenario (summary) from LimaRP yaml source data. To some extent it can work with different text types, however.

Prompt format

### Input:
{Your text here}

### Response:
Charactername's Persona: {output goes here}

Replace Charactername with the name of the character you want to infer a Persona for. By default this LoRA looks for the placeholder names <FIRST> and <SECOND> (in this respective order) but it can work with proper names as well.

Example

This image shows what would happen (red box) after adding data in the format shown in the left pane.

In practice the results would be double-checked and manually tweaked to diversify the outputs and adding character quirks, peculiarities or traits that the model couldn't catch.

Known issues

While the scenario/summary is often remarkably accurate, personas don't show a very high accuracy and can be repetitive.
Persona and Scenario may exhibit gpt-isms.
Peculiar character quirks may not be observed by the model.
The LoRA hasn't been extensively tested with different input formats.
There are apparently issues with the EOS token getting generated too early. It's suggested to disable it.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00025
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.992	0.06	15	1.8884
1.8026	0.12	30	1.8655
1.7713	0.19	45	1.8539
1.7145	0.25	60	1.8502
1.6686	0.31	75	1.8507
1.8409	0.37	90	1.8469
1.7741	0.44	105	1.8434
1.7384	0.5	120	1.8407
1.7562	0.56	135	1.8390
1.7392	0.62	150	1.8373
1.8735	0.68	165	1.8381
1.8406	0.75	180	1.8377
1.6602	0.81	195	1.8350
1.7803	0.87	210	1.8341
1.7212	0.93	225	1.8329
1.8126	1.0	240	1.8330
1.8776	1.06	255	1.8314
1.7892	1.12	270	1.8328
1.7029	1.18	285	1.8338
1.7094	1.24	300	1.8322
1.7921	1.31	315	1.8310
1.8309	1.37	330	1.8316
1.7373	1.43	345	1.8309
1.7873	1.49	360	1.8313
1.7151	1.56	375	1.8306
1.7529	1.62	390	1.8300
1.7516	1.68	405	1.8293
1.7704	1.74	420	1.8294
1.6351	1.8	435	1.8290
1.6186	1.87	450	1.8291
1.7086	1.93	465	1.8295
1.6595	1.99	480	1.8290

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.0

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