Instructions to use Banaxi-Tech/test-pico-chinchilla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Banaxi-Tech/test-pico-chinchilla with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Banaxi-Tech/test-pico-chinchilla", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Banaxi-Tech/test-pico-chinchilla", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Banaxi-Tech/test-pico-chinchilla with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Banaxi-Tech/test-pico-chinchilla"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Banaxi-Tech/test-pico-chinchilla",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Banaxi-Tech/test-pico-chinchilla

SGLang

How to use Banaxi-Tech/test-pico-chinchilla 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 "Banaxi-Tech/test-pico-chinchilla" \
    --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": "Banaxi-Tech/test-pico-chinchilla",
		"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 "Banaxi-Tech/test-pico-chinchilla" \
        --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": "Banaxi-Tech/test-pico-chinchilla",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Banaxi-Tech/test-pico-chinchilla with Docker Model Runner:
```
docker model run hf.co/Banaxi-Tech/test-pico-chinchilla
```

BananaMind 2 Pico — Chinchilla Test

Freshly initialized 900,002-parameter Pico model trained to the Chinchilla budget of 20 tokens per parameter. This is a controlled short-run comparison, not a continuation of Banaxi-Tech/pico-test.

Field	Value
Parameters	900,002
Training tokens	18,000,040
Tokens per parameter	20.000000
Context / vocabulary	4,096 / 384
Architecture	6L, 96D, XSA refresh on layers 4 and 6
Data	70% FineWeb-HQ, 30% Cosmopedia v2
Optimizers	Muon 0.07 + AdamW 0.004
Precision / hardware	bfloat16 autocast / NVIDIA RTX PRO 6000 Blackwell Server Edition
Template revision	`5fa6920cf5ae031f03bd044a4de12564ec453837`

Load with trust_remote_code=True.

Downloads last month: -

Safetensors

Model size

937k params

Tensor type

F32

Banaxi-Tech
/

test-pico-chinchilla

BananaMind 2 Pico — Chinchilla Test

Datasets used to train Banaxi-Tech/test-pico-chinchilla