Instructions to use Gryphe/MythoLogic-Mini-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gryphe/MythoLogic-Mini-7b with Transformers:

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

pipe = pipeline("text-generation", model="Gryphe/MythoLogic-Mini-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gryphe/MythoLogic-Mini-7b")
model = AutoModelForCausalLM.from_pretrained("Gryphe/MythoLogic-Mini-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Gryphe/MythoLogic-Mini-7b with 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

SGLang

How to use Gryphe/MythoLogic-Mini-7b 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 "Gryphe/MythoLogic-Mini-7b" \
    --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": "Gryphe/MythoLogic-Mini-7b",
		"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 "Gryphe/MythoLogic-Mini-7b" \
        --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": "Gryphe/MythoLogic-Mini-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gryphe/MythoLogic-Mini-7b with Docker Model Runner:
```
docker model run hf.co/Gryphe/MythoLogic-Mini-7b
```

Gryphe commited on Aug 4, 2023

Commit

43f822e

1 Parent(s): d920445

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@@ -21,23 +21,11 @@ Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheB
 Due to its Hermes-2 core this model works best with Alpaca formatting, so for optimal model performance, use:
 ```
-### Instruction:
-Your instruction or question here.
-### Response:
-```
-For chat/roleplay conversations, I suggest the following structure instead:
-```
-You are <AI NAME>, a friendly and supportive AI language model. We discuss all subjects freely and without bias or restrictions.
-I am <YOUR NAME>, the user interacting with you through a chat conversation. Start with greeting me.
 ### Instruction:
-Write <AI NAME>'s next reply in a chat between <YOUR NAME> and <AI NAME>. Write a single reply only.
-### Chat History:
-<AI NAME>: Good day, <YOUR NAME>! How can I assist you today?
-(Etc, etc)
 ### Response:
-<AI NAME>:
 ```

 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:
 ```