Instructions to use Gryphe/Pantheon-RP-1.0-8b-Llama-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gryphe/Pantheon-RP-1.0-8b-Llama-3 with Transformers:

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

pipe = pipeline("text-generation", model="Gryphe/Pantheon-RP-1.0-8b-Llama-3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gryphe/Pantheon-RP-1.0-8b-Llama-3")
model = AutoModelForCausalLM.from_pretrained("Gryphe/Pantheon-RP-1.0-8b-Llama-3", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gryphe/Pantheon-RP-1.0-8b-Llama-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gryphe/Pantheon-RP-1.0-8b-Llama-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gryphe/Pantheon-RP-1.0-8b-Llama-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gryphe/Pantheon-RP-1.0-8b-Llama-3

SGLang

How to use Gryphe/Pantheon-RP-1.0-8b-Llama-3 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/Pantheon-RP-1.0-8b-Llama-3" \
    --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": "Gryphe/Pantheon-RP-1.0-8b-Llama-3",
		"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 "Gryphe/Pantheon-RP-1.0-8b-Llama-3" \
        --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": "Gryphe/Pantheon-RP-1.0-8b-Llama-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Gryphe/Pantheon-RP-1.0-8b-Llama-3 with Docker Model Runner:
```
docker model run hf.co/Gryphe/Pantheon-RP-1.0-8b-Llama-3
```

Gryphe commited on May 13, 2024

Commit

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@@ -22,14 +22,14 @@ I originally never intended to publish this model but over time I've become curi
 Quantized versions are available from Bartowski: [GGUF](https://huggingface.co/bartowski/Pantheon-RP-1.0-8b-Llama-3-GGUF) - [EXL2](https://huggingface.co/bartowski/Pantheon-RP-1.0-8b-Llama-3-exl2)
 ## Model details
-This model features a highly diverse collection of datasets, totaling ~22 million tokens;
 - For general instructions I created GPT 4 and Claude Opus variations of the No-Robots dataset. I actually ended up not including NoRo itself as it made the model worse.
 - For roleplay I used an extensive collection of GPT 4 and Claude Opus data, augmented by the always popular LimaRP for the "human factor".
 - The Pantheon Roleplay personas were made using Claude 1.3 data, further diversifying the outputs of this model.
 - Aiva's persona includes additional datasets featuring questions related to DM world building, Python coding and RSS summarization. (She summarizes my daily news every day!)
-Roughly 25% of the training data was instructional, with the rest being focused on roleplay. Each of these datasets was then carefully balanced to ensure diversity, removing examples where deemed necessary.
 **TLDR;** Download. ChatML prompt format. Have fun! Leave feedback!

 Quantized versions are available from Bartowski: [GGUF](https://huggingface.co/bartowski/Pantheon-RP-1.0-8b-Llama-3-GGUF) - [EXL2](https://huggingface.co/bartowski/Pantheon-RP-1.0-8b-Llama-3-exl2)
 ## Model details
+This model features a highly diverse collection of datasets, totaling ~24 million tokens;
 - For general instructions I created GPT 4 and Claude Opus variations of the No-Robots dataset. I actually ended up not including NoRo itself as it made the model worse.
 - For roleplay I used an extensive collection of GPT 4 and Claude Opus data, augmented by the always popular LimaRP for the "human factor".
 - The Pantheon Roleplay personas were made using Claude 1.3 data, further diversifying the outputs of this model.
 - Aiva's persona includes additional datasets featuring questions related to DM world building, Python coding and RSS summarization. (She summarizes my daily news every day!)
+Roughly 30% of the training data was instructional, with another 25% being used by the Pantheon Persona data. The remaining 45% was filled with roleplay scenarios covering a huge spectrum of situations. Each of these datasets was then carefully balanced to ensure diversity, removing examples where deemed necessary.
 **TLDR;** Download. ChatML prompt format. Have fun! Leave feedback!