Instructions to use Q-bert/Merged-AGI-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q-bert/Merged-AGI-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Q-bert/Merged-AGI-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Q-bert/Merged-AGI-7B") model = AutoModelForCausalLM.from_pretrained("Q-bert/Merged-AGI-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Q-bert/Merged-AGI-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Q-bert/Merged-AGI-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Q-bert/Merged-AGI-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Q-bert/Merged-AGI-7B
- SGLang
How to use Q-bert/Merged-AGI-7B 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 "Q-bert/Merged-AGI-7B" \ --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": "Q-bert/Merged-AGI-7B", "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 "Q-bert/Merged-AGI-7B" \ --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": "Q-bert/Merged-AGI-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Q-bert/Merged-AGI-7B with Docker Model Runner:
docker model run hf.co/Q-bert/Merged-AGI-7B
Slerp Merging
It's incredible that you can merge 4 LLMs together yet the outputs remain coherent. I'm assuming that has something to do with how much better spherical linear interpolation is compared to weight averaging.
This got me wondering if any combination of Mistrals can be merged? Are there compatibility issues (e.g. tokens)? Do you need to get permission first? Just wondering because the "smartest" Mistral I've come across is Dolphin 2.1, while the one that produced the most human-like responses is Starling alpha. Is there a reason these two couldn't be merged?
Edit: I guess this has kinda been done. Was looking around and OpenHermes2.5 is very similar to Dolphin 2.1, and neural chat is similar to Starling. https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
It's incredible that you can merge 4 LLMs together yet the outputs remain coherent. I'm assuming that has something to do with how much better spherical linear interpolation is compared to weight averaging.
This got me wondering if any combination of Mistrals can be merged? Are there compatibility issues (e.g. tokens)? Do you need to get permission first? Just wondering because the "smartest" Mistral I've come across is Dolphin 2.1, while the one that produced the most human-like responses is Starling alpha. Is there a reason these two couldn't be merged?
Edit: I guess this has kinda been done. Was looking around and OpenHermes2.5 is very similar to Dolphin 2.1, and neural chat is similar to Starling. https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
Good idea. I'm gonna this.