Instructions to use wave-on-discord/silly-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wave-on-discord/silly-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="wave-on-discord/silly-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wave-on-discord/silly-v0.2")
model = AutoModelForCausalLM.from_pretrained("wave-on-discord/silly-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wave-on-discord/silly-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wave-on-discord/silly-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wave-on-discord/silly-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wave-on-discord/silly-v0.2

SGLang

How to use wave-on-discord/silly-v0.2 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 "wave-on-discord/silly-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wave-on-discord/silly-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "wave-on-discord/silly-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wave-on-discord/silly-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wave-on-discord/silly-v0.2 with Docker Model Runner:
```
docker model run hf.co/wave-on-discord/silly-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

silly-v0.2

Finetune of Mistral-Nemo-Base-2407 designed to emulate the writing style of character.ai models.

2 epochs of SFT on RP data, then about an hour of PPO on 8xH100 with POLAR-7B RFT
Kind of wonky, if you're dealing with longer messages you may need to decrease your temperature
ChatML chat format
Reviews:

its typically good at writing, v good for 12b, coherent in RP, follows context and starts conversations well

I do legit like it, it feels good to use. When it gives me stable output the output is high quality and on task, its got small model stupid where basic logic holds but it invents things or forgets them (feels like small effective context window maybe?) which, to be clear, is like. Perfectly fine. Very good st synthesizing and inferring information provided in context on a higher level

This is mostly a proof-of-concept, showcasing that POLAR reward models can be very useful for "out of distribution" tasks like roleplaying. If you're working on your own roleplay finetunes, please consider using POLAR!

Downloads last month: 10

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for wave-on-discord/silly-v0.2

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

(86)

this model

Quantizations

9 models