Instructions to use 922CA/gem-monika-ddlc-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 922CA/gem-monika-ddlc-2b with Transformers:

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

pipe = pipeline("text-generation", model="922CA/gem-monika-ddlc-2b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("922CA/gem-monika-ddlc-2b")
model = AutoModelForCausalLM.from_pretrained("922CA/gem-monika-ddlc-2b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 922CA/gem-monika-ddlc-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "922CA/gem-monika-ddlc-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "922CA/gem-monika-ddlc-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/922CA/gem-monika-ddlc-2b

SGLang

How to use 922CA/gem-monika-ddlc-2b 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 "922CA/gem-monika-ddlc-2b" \
    --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": "922CA/gem-monika-ddlc-2b",
		"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 "922CA/gem-monika-ddlc-2b" \
        --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": "922CA/gem-monika-ddlc-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use 922CA/gem-monika-ddlc-2b with Docker Model Runner:
```
docker model run hf.co/922CA/gem-monika-ddlc-2b
```

922CA commited on Apr 24, 2025

Commit

d21d34b

verified ·

1 Parent(s): 2bf5da1

Update README.md

Browse files

Files changed (1) hide show

README.md +24 -7

README.md CHANGED Viewed

@@ -11,14 +11,31 @@ tags:
 - trl
 ---
-Test
-# Uploaded  model
-- **Developed by:** 922-CA
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
-This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - trl
 ---
+# gem-monika-ddlc-2b (AKA Lilmonix2b-v1):
+* [Fine-tune](https://huggingface.co/unsloth/gemma-2-2b-bnb-4bit) for Monika character from DDLC
+* Fine-tuned on a [dataset of ~600+ items](https://huggingface.co/datasets/922-CA/MoCha_v1) (dialogue scraped from game, reddit, and Twitter synthetically augmented by turn each into snippets of multi-turn chat dialogue between Player and Monika; this was then manually edited, with more manually crafted items including info about character added in)
+* [GG](https://huggingface.co/922CA/gem-monika-ddlc-2b-gguf)[UF](https://huggingface.co/mradermacher/gem-monika-ddlc-2b-GGUF)
+# 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
+* Tuned for 1 epoch
+* rank: 32
+* lora alpha: 32
+* lora dropout: 0.5
+* lr: 2e-4
+* batch size: 2
+* warmup ratio: 0.1
+* grad steps: 4
+# WARNINGS AND DISCLAIMERS
+This model is meant to closely reflect the characteristics of Monika. Despite this, there is always the chance that "Monika" will hallucinate and get information about herself wrong or act out of character.
+Additionally, being character-focused means that this model may have lost some assistant capability for some specific tasks.
+Finally, this model is not guaranteed to output aligned or safe outputs, use at your own risk!