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 Jul 29, 2023

Commit

d920445

1 Parent(s): 4fa77a9

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@@ -15,6 +15,8 @@ Since 7B models tend to be less capable all-rounders, more emphasis was put on i
 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.
 ## Prompt Format
 Due to its Hermes-2 core this model works best with Alpaca formatting, so for optimal model performance, use:

 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](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-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: