Instructions to use JayhC/L3-Aethora-15B-8bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JayhC/L3-Aethora-15B-8bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="JayhC/L3-Aethora-15B-8bpw-h8-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JayhC/L3-Aethora-15B-8bpw-h8-exl2")
model = AutoModelForMultimodalLM.from_pretrained("JayhC/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JayhC/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JayhC/L3-Aethora-15B-8bpw-h8-exl2

SGLang

How to use JayhC/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-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/L3-Aethora-15B-8bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JayhC/L3-Aethora-15B-8bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/JayhC/L3-Aethora-15B-8bpw-h8-exl2
```

8bpw/h8 exl2 quantization of Steelskull/L3-Aethora-15B using exllamav2 v0.1.4 and default calibration dataset.

ORIGINAL CARD:

L3-Aethora-15B Data Card

The Skullery Presents L3-Aethora-15B.

Creator: Steelskull

Dataset: Aether-Lite-V1.2

Trained: 4 x A100 for 15 hours Using RsLora and DORA

About L3-Aethora-15B:

 L3 = Llama3

L3-Aethora-15B was crafted through using the abilteration method to adjust model responses. The model's refusal is inhibited, focusing on yielding more compliant and facilitative dialogue interactions. It then underwent a modified DUS (Depth Up Scale) merge (originally used by @Elinas) by using passthrough merge to create a 15b model, with specific adjustments (zeroing) to 'o_proj' and 'down_proj', enhancing its efficiency and reducing perplexity. This created AbL3In-15b.

AbL3In-15b was then trained for 4 epochs using Rslora & DORA training methods on the Aether-Lite-V1.2 dataset, containing ~82000 high quality samples, designed to strike a fine balance between creativity, slop, and intelligence at about a 60/40 split

This model is trained on the L3 prompt format.

Quants:

Mradermacher/L3-Aethora-15B-GGUF

NikolayKozloff/L3-Aethora-15B-GGUF

Dataset Summary: (Filtered)

Filtered Phrases: GPTslop, Claudism's

mrfakename/Pure-Dove-ShareGPT: Processed 3707, Removed 150
mrfakename/Capybara-ShareGPT: Processed 13412, Removed 2594
jondurbin/airoboros-3.2: Processed 54517, Removed 4192
PJMixers/grimulkan_theory-of-mind-ShareGPT: Processed 533, Removed 6
grimulkan/PIPPA-augmented-dedup: Processed 869, Removed 46
grimulkan/LimaRP-augmented: Processed 790, Removed 14
PJMixers/grimulkan_physical-reasoning-ShareGPT: Processed 895, Removed 4
MinervaAI/Aesir-Preview: Processed 994, Removed 6
Doctor-Shotgun/no-robots-sharegpt: Processed 9911, Removed 89

Deduplication Stats:

Starting row count: 85628, Final row count: 81960, Rows removed: 3668

Downloads last month: 1

JayhC
/

L3-Aethora-15B-8bpw-h8-exl2

L3-Aethora-15B

About L3-Aethora-15B:

Quants:

Dataset Summary: (Filtered)

Deduplication Stats:

Dataset used to train JayhC/L3-Aethora-15B-8bpw-h8-exl2