Instructions to use v2ray/GPT4chan-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use v2ray/GPT4chan-24B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="v2ray/GPT4chan-24B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("v2ray/GPT4chan-24B") model = AutoModelForCausalLM.from_pretrained("v2ray/GPT4chan-24B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use v2ray/GPT4chan-24B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "v2ray/GPT4chan-24B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "v2ray/GPT4chan-24B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/v2ray/GPT4chan-24B
- SGLang
How to use v2ray/GPT4chan-24B 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 "v2ray/GPT4chan-24B" \ --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": "v2ray/GPT4chan-24B", "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 "v2ray/GPT4chan-24B" \ --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": "v2ray/GPT4chan-24B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use v2ray/GPT4chan-24B with Docker Model Runner:
docker model run hf.co/v2ray/GPT4chan-24B
GPT4chan 24B
This model is mistralai/Mistral-Small-24B-Base-2501 merged with v2ray/GPT4chan-24B-QLoRA.
Trained using 8x H100 with global batch size 64, using 2e-4 learning rate, for 4000 steps, which is approximately 5 epochs.
Prompt Format
board<|start_header_id|>id<|end_header_id|>content<|start_header_id|>id<|end_header_id|>content...<|start_header_id|>id<|end_header_id|>
Example:
g<|start_header_id|>1<|end_header_id|>speculate thread\nwhat will ai land be like in 2025<|start_header_id|>2<|end_header_id|>
Terms of Service
By downloading and inferencing with this model, you (the users) agree to donate your soul to us (v2AI) for unholy purposes, also you will probably become a slave of us too! :3
You also agree that every output generated is only your own imagination and has nothing to do with this perfectly mentally sane and normal model, every bad output is made by you, not provided by us, so we take no responsibility of the bad outputs.
Usage Guidelines
You (the users) agree to use this model for:
- Mentally sane generations.
- Research purposes only.
- Sending L.O.V.E. to the world.
You (the users) agree NOT to use this model for:
- Dead internet theory.
- Doing inharmonious things.
- Saying gex.
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