Text Generation
Transformers
Safetensors
English
mixtral
Mixture of Experts
booper
pretraining
conversational
text-generation-inference
Instructions to use ProCreations/Booper-Big with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/Booper-Big with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ProCreations/Booper-Big") 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("ProCreations/Booper-Big") model = AutoModelForCausalLM.from_pretrained("ProCreations/Booper-Big", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ProCreations/Booper-Big with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ProCreations/Booper-Big" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ProCreations/Booper-Big", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ProCreations/Booper-Big
- SGLang
How to use ProCreations/Booper-Big 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 "ProCreations/Booper-Big" \ --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": "ProCreations/Booper-Big", "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 "ProCreations/Booper-Big" \ --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": "ProCreations/Booper-Big", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ProCreations/Booper-Big with Docker Model Runner:
docker model run hf.co/ProCreations/Booper-Big
Download training_metadata.json from ProCreations/Booper-Big: direct link, hf CLI and curl.
- Browser
- Download file 798 Bytes
-
https://huggingface.co/ProCreations/Booper-Big/resolve/main/training_metadata.json
- Command line
-
hf download hf://ProCreations/Booper-Big/training_metadata.json
-
curl -L -o training_metadata.json https://huggingface.co/ProCreations/Booper-Big/resolve/main/training_metadata.json
798 Bytes
| { | |
| "active_parameters_per_token": 50512000, | |
| "architecture": { | |
| "hidden_size": 896, | |
| "intermediate_size": 1024, | |
| "max_position_embeddings": 4096, | |
| "num_attention_heads": 14, | |
| "num_experts_per_tok": 1, | |
| "num_hidden_layers": 6, | |
| "num_key_value_heads": 14, | |
| "num_local_experts": 7, | |
| "vocab_size": 16384 | |
| }, | |
| "assistant_target_tokens": null, | |
| "completed_budget": true, | |
| "context_window": 4096, | |
| "dataset": "openbmb/Ultra-FineWeb (en, score>=0.70)", | |
| "elapsed_seconds": 14777.457427978516, | |
| "finished_at": "2026-08-26T07:11:20Z", | |
| "precision": "bf16 autocast with fp32 master weights", | |
| "qat": false, | |
| "qat_scheme": null, | |
| "sequence_length_trained": 1024, | |
| "stage": "pretrain", | |
| "steps": 50863, | |
| "tokens_trained": 5000036352, | |
| "total_parameters": 149602432 | |
| } | |