Instructions to use JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2

SGLang

How to use JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2 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 "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2" \
    --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": "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2",
		"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 "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2" \
        --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": "JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/JayhC/Llama-3-Soliloquy-Max-70B-v1-3bpw-h6-exl2
```

3bpw/h6 exl2 quantization of openlynn/Llama-3-Soliloquy-Max-70B-v1 using default exllamav2 calibration dataset.

ORIGINAL CARD:

LYNN - AI for Roleplay

Soliloquy-L3

Soliloquy-L3 is a fast, highly capable roleplaying model designed for immersive, dynamic experiences. Trained on over 250 million tokens of roleplaying data, Soliloquy-L3 has a vast knowledge base, rich literary expression, and support for up to 32k context length.

Model Info

Context Length	Parameter	Prompt Template	isErp
32k(32768)	70B	Llama 3 Chat	Partly

Prompt Template

Use can you following jinja2 template. Which is identical to chat_template in tokenizer_config.

{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}

Llama 3 Intended Use

Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.

Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.

**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.

https://llama.meta.com/llama3/license

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