Instructions to use ProCreations/Booper-Big with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Booper-Big / README.md

ProCreations

Publish Booper-Big

ba40e5d verified 2 months ago

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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- openbmb/Ultra-FineWeb
tags:
- moe
- booper
- pretraining
---
# Booper-Big

Booper-Big is a from-scratch English MoE language model trained on the English split of
`openbmb/Ultra-FineWeb`. It uses the standard Hugging Face Mixtral implementation.

| Property | Value |
|---|---:|
| Total parameters | 149,602,432 |
| Active parameters per token | 50,512,000 |
| Layers / width / heads | 6 / 896 / 14 |
| Experts / active experts | 7 / 1 |
| Context window | 4,096 |
| Tokenizer | Booper byte-BPE, 16,384 tokens |
| Pretraining tokens | 5,000,036,352 |
| Training precision | BF16 compute, FP32 master weights; BF16 published weights |
| Final training loss | 1.8922 |
| Held-out Ultra-FineWeb loss | 1.5720 |

The training stream filtered the already-curated English split to quality score ≥0.70 and held
the last prepared shard out of training. The model was trained with 1,024-token packed sequences;
RoPE and the published configuration support a 4,096-token context window.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ProCreations/Booper-Big"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
```

This is a small base model, not an instruction-following assistant. Outputs may be inaccurate or unsafe.

	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	datasets:
	- openbmb/Ultra-FineWeb
	tags:
	- moe
	- booper
	- pretraining
	---

	# Booper-Big

	Booper-Big is a from-scratch English MoE language model trained on the English split of
	`openbmb/Ultra-FineWeb`. It uses the standard Hugging Face Mixtral implementation.

	\| Property \| Value \|
	\|---\|---:\|
	\| Total parameters \| 149,602,432 \|
	\| Active parameters per token \| 50,512,000 \|
	\| Layers / width / heads \| 6 / 896 / 14 \|
	\| Experts / active experts \| 7 / 1 \|
	\| Context window \| 4,096 \|
	\| Tokenizer \| Booper byte-BPE, 16,384 tokens \|
	\| Pretraining tokens \| 5,000,036,352 \|
	\| Training precision \| BF16 compute, FP32 master weights; BF16 published weights \|
	\| Final training loss \| 1.8922 \|
	\| Held-out Ultra-FineWeb loss \| 1.5720 \|

	The training stream filtered the already-curated English split to quality score ≥0.70 and held
	the last prepared shard out of training. The model was trained with 1,024-token packed sequences;
	RoPE and the published configuration support a 4,096-token context window.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "ProCreations/Booper-Big"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
	```

	This is a small base model, not an instruction-following assistant. Outputs may be inaccurate or unsafe.