Text Generation
Transformers
Safetensors
llama
llama-factory
conversational
text-generation-inference
Instructions to use SteelStorage/L3-Aethora-15B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SteelStorage/L3-Aethora-15B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SteelStorage/L3-Aethora-15B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SteelStorage/L3-Aethora-15B") model = AutoModelForCausalLM.from_pretrained("SteelStorage/L3-Aethora-15B", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SteelStorage/L3-Aethora-15B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SteelStorage/L3-Aethora-15B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SteelStorage/L3-Aethora-15B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SteelStorage/L3-Aethora-15B
- SGLang
How to use SteelStorage/L3-Aethora-15B 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 "SteelStorage/L3-Aethora-15B" \ --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": "SteelStorage/L3-Aethora-15B", "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 "SteelStorage/L3-Aethora-15B" \ --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": "SteelStorage/L3-Aethora-15B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SteelStorage/L3-Aethora-15B with Docker Model Runner:
docker model run hf.co/SteelStorage/L3-Aethora-15B
|
Download README.md from SteelStorage/L3-Aethora-15B: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/SteelStorage/L3-Aethora-15B/resolve/1db424f3fda170fceaf9511931bb9002e054f6c3/README.md
- Command line
-
hf download hf://SteelStorage/L3-Aethora-15B@1db424f3fda170fceaf9511931bb9002e054f6c3/README.md
-
curl -L -o README.md https://huggingface.co/SteelStorage/L3-Aethora-15B/resolve/1db424f3fda170fceaf9511931bb9002e054f6c3/README.md
4.16 kB
| library_name: transformers | |
| tags: | |
| - llama-factory | |
| license: llama3 | |
| datasets: | |
| - TheSkullery/Aether-Lite-V1.2 | |
| <!DOCTYPE html> | |
| <style> | |
| body { | |
| font-family: 'Quicksand', sans-serif; | |
| background: linear-gradient(135deg, #2E3440 0%, #1A202C 100%); | |
| color: #D8DEE9; | |
| margin: 0; | |
| padding: 0; | |
| font-size: 16px; | |
| } | |
| .container { | |
| width: 80% auto; | |
| max-width: 1080px auto; | |
| margin: 20px auto; | |
| background-color: rgba(255, 255, 255, 0.02); | |
| padding: 20px; | |
| border-radius: 12px; | |
| box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2); | |
| backdrop-filter: blur(10px); | |
| border: 1px solid rgba(255, 255, 255, 0.1); | |
| } | |
| .header h1 { | |
| font-size: 28px; | |
| color: #ECEFF4; | |
| margin: 0 0 20px 0; | |
| text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); | |
| } | |
| .update-section { | |
| margin-top: 30px; | |
| } | |
| .update-section h2 { | |
| font-size: 24px; | |
| color: #88C0D0; | |
| } | |
| .update-section p { | |
| font-size: 16px; | |
| line-height: 1.6; | |
| color: #ECEFF4; | |
| } | |
| .info img { | |
| width: 100%; | |
| border-radius: 10px; | |
| margin-bottom: 15px; | |
| } | |
| a { | |
| color: #88C0D0; | |
| text-decoration: none; | |
| } | |
| a:hover { | |
| color: #A3BE8C; | |
| } | |
| .button { | |
| display: inline-block; | |
| background-color: #5E81AC; | |
| color: #E5E9F0; | |
| padding: 10px 20px; | |
| border-radius: 5px; | |
| cursor: pointer; | |
| text-decoration: none; | |
| } | |
| .button:hover { | |
| background-color: #81A1C1; | |
| } | |
| pre { | |
| background-color: #2E3440; | |
| padding: 10px; | |
| border-radius: 5px; | |
| overflow-x: auto; | |
| } | |
| code { | |
| font-family: 'Courier New', monospace; | |
| color: #D8DEE9; | |
| } | |
| </style> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>L3-Aethora-15B Data Card</title> | |
| <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="header"> | |
| <h1>L3-Aethora-15B</h1> | |
| </div> | |
| <div class="info"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/FkCJ5U8twXsYuohVTi1t2.webp"> | |
| <p>The Skullery Presents L3-Aethora-15B.</p> | |
| <p><strong>Creator:</strong> <a href="https://huggingface.co/steelskull" target="_blank">Steelskull</a></p> | |
| <p><strong>Dataset:</strong> <a href="https://huggingface.co/datasets/TheSkullery/Aether-Lite-V1.2" target="_blank">Aether-Lite-V1.2</a></p> | |
| <h1>About L3-Aethora-15B:</h1> | |
| <p>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 underwent a specialized merging process (originally used by @Elias) by using passthrough merge to create a 15b model, with specific adjustments to 'o_proj' and 'down_proj' settings, enhancing its efficiency and reducing perplexity. This created AbL3In-15b.<br> | |
| <p>AbL3In-15b was then fine-tuned on the Aether-Lite-V1 dataset, containing ~82000 high quality samples, designed to strike a fine balance between creativity, slop, and intelligence at about a 60/40 split</p> | |
| <p>Filtered Phrases: GPTslop, Claudism's</p> | |
| <h2>Dataset Summary: (Filtered)</h2> | |
| <ul> | |
| <li><strong>mrfakename/Pure-Dove-ShareGPT:</strong> Processed 3707, Removed 150</li> | |
| <li><strong>mrfakename/Capybara-ShareGPT:</strong> Processed 13412, Removed 2594</li> | |
| <li><strong>jondurbin/airoboros-3.2:</strong> Processed 54517, Removed 4192</li> | |
| <li><strong>PJMixers/grimulkan_theory-of-mind-ShareGPT:</strong> Processed 533, Removed 6</li> | |
| <li><strong>grimulkan/PIPPA-augmented-dedup:</strong> Processed 869, Removed 46</li> | |
| <li><strong>grimulkan/LimaRP-augmented:</strong> Processed 790, Removed 14</li> | |
| <li><strong>PJMixers/grimulkan_physical-reasoning-ShareGPT:</strong> Processed 895, Removed 4</li> | |
| <li><strong>MinervaAI/Aesir-Preview:</strong> Processed 994, Removed 6</li> | |
| <li><strong>Doctor-Shotgun/no-robots-sharegpt:</strong> Processed 9911, Removed 89</li> | |
| </ul> | |
| <h2>Deduplication Stats:</h2> | |
| <p>Starting row count: 85628, Final row count: 81960, Rows removed: 3668</p> | |
| </div> | |
| </div> | |
| </body> | |
| </html> |