How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gryphe/MythoLogic-Mini-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gryphe/MythoLogic-Mini-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/Gryphe/MythoLogic-Mini-7b
Quick Links

Model details

MythoLogic-Mini-7b can be considered the little brother in my Mytho series of models: MythoLogic-13b and MythoBoros-13b).

Its Llama-2 core is powered by Nous Hermes-2, which is further augmented by Stable Beluga and a carefully distilled Kimiko LoRa.

Since 7B models tend to be less capable all-rounders, more emphasis was put on improving the roleplaying aspects for this gradient merge, of which various gradients were benchmarked before settling on the configuration shown below.

In technical terms, the Hermes-2 core starts at 90% strength before fading away completely at the 12th layer level, where Stable Beluga (and Kimiko) handle the more intricate linguistic aspects.

Quantized models are available from TheBloke: GGML - GPTQ (You're the best!)

Prompt Format

Due to its Hermes-2 core this model works best with Alpaca formatting, so for optimal model performance, use:

<System prompt/Character Card>

### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.

### Response:
Downloads last month
12
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Gryphe/MythoLogic-Mini-7b

Finetunes
1 model
Quantizations
5 models

Collection including Gryphe/MythoLogic-Mini-7b