Instructions to use Gryphe/MythoLogic-L2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gryphe/MythoLogic-L2-13b with Transformers:

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

pipe = pipeline("text-generation", model="Gryphe/MythoLogic-L2-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gryphe/MythoLogic-L2-13b")
model = AutoModelForCausalLM.from_pretrained("Gryphe/MythoLogic-L2-13b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Gryphe/MythoLogic-L2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gryphe/MythoLogic-L2-13b"
# 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-L2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gryphe/MythoLogic-L2-13b

SGLang

How to use Gryphe/MythoLogic-L2-13b 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-L2-13b" \
    --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-L2-13b",
		"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-L2-13b" \
        --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-L2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gryphe/MythoLogic-L2-13b with Docker Model Runner:
```
docker model run hf.co/Gryphe/MythoLogic-L2-13b
```

Gryphe commited on Aug 4, 2023

Commit

7b2e476

1 Parent(s): b331d33

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@@ -1,3 +1,45 @@
 ---
 license: other
 ---

 ---
 license: other
+language:
+- en
 ---
+The Llama 2 sequel to my original experiment with gradient merges using [the following script](https://github.com/Gryphe/BlockMerge_Gradient). Its three models ([Hermes](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b), [Chronos](https://huggingface.co/elinas/chronos-13b-v2) and [Airoboros](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-2.0)) are almost evenly divided over the layer structure this time. Airoboros was the "wildcard model" due to its superior ability to understand complex instructions.
+Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoLogic-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoLogic-13B-GPTQ) (You're the best!)
+## Model details
+As always, the main objective was to create an all-round model with improved roleplaying capabilities. MythoLogic-L2 differs from its predecessor in that it focuses primarily on the understanding of instructions and personalities of complex character cards.
+Illustrated below are the gradients used for this L2 recipe;
+[](approximation.png)
+## Prompt Format
+This model primarily uses (and wast tested 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>:
+```
+---
+license: other
+---