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

Libraries

How to use Banaxi-Tech/custom-optimizer-model-test with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Banaxi-Tech/custom-optimizer-model-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Banaxi-Tech/custom-optimizer-model-test"
# 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/custom-optimizer-model-test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Banaxi-Tech/custom-optimizer-model-test

SGLang

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

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

custom-optimizer-model-test / README.md

Banaxi-Tech

Upload Aspect-Cautious Muon checkpoint at 24,999,591,936 tokens

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	datasets:
	- HuggingFaceFW/fineweb-edu
	tags:
	- causal-lm
	- base-model
	- bananamind2-nano
	- custom-optimizer
	- aspect-cautious-muon
	- fineweb-edu
	- optimizer-comparison
	- custom-code
	- trust-remote-code
	---

	# BananaMind 2 Nano Custom Optimizer Test

	This experimental base model uses the exact BananaMind 2 Nano architecture
	and tokenizer. It was trained from scratch with Aspect-Cautious Muon, an
	experimental optimizer that combines stock PyTorch Muon with a small cautious
	Adam residual on hidden matrices. The tied embedding and normalization weights
	use AdamW. Training used only streamed FineWeb-Edu data for
	24,999,591,936 custom-tokenizer tokens.

	## Architecture

	\| Field \| Value \|
	\|---\|---:\|
	\| Parameters \| 9,968,128 \|
	\| Layers \| 10 \|
	\| Hidden size \| 256 \|
	\| Intermediate size \| 768 \|
	\| Query heads \| 4 \|
	\| KV heads \| 2 \|
	\| Head dimension \| 64 \|
	\| Context \| 4,096 \|
	\| Vocabulary \| 8,192 \|
	\| Embeddings \| Tied \|
	\| Attention \| GQA, pre-RoPE QK norm \|
	\| MLP \| SwiGLU \|
	\| Position encoding \| RoPE, theta 100,000 \|

	## Training

	\| Field \| Value \|
	\|---\|---:\|
	\| Dataset \| `HuggingFaceFW/fineweb-edu` / `sample-100BT` \|
	\| Dataset revision \| `87f09149ef4734204d70ed1d046ddc9ca3f2b8f9` \|
	\| Data access \| Streaming \|
	\| Hidden-matrix optimizer \| Aspect-Cautious Muon \|
	\| Muon peak learning rate \| 0.05 \|
	\| Muon momentum \| 0.95, Nesterov \|
	\| Muon Newton-Schulz steps \| 5 \|
	\| Cautious Adam residual peak LR \| 0.0003 \|
	\| Residual aspect scaling \| `min(2, sqrt(long_side / short_side))` \|
	\| Embedding/norm optimizer \| AdamW \|
	\| AdamW peak learning rate \| 0.003 \|
	\| AdamW betas \| (0.9, 0.95) \|
	\| Global batch \| 132 sequences \|
	\| Tokens per optimizer step \| 540,672 \|
	\| Optimizer steps \| 46,238 \|
	\| Warmup \| 1,750 steps \|
	\| Schedule \| Warmup-stable-decay, final 15% cosine cooldown \|
	\| Weight decay \| 0.1, then 0.01 after 12,000,000,000 tokens \|
	\| Precision \| bfloat16 autocast, float32 master weights \|
	\| Hardware \| 8 x NVIDIA RTX PRO 6000 Blackwell Server Edition \|
	\| Seed \| 1337 \|

	The original Nano effective batch was 12 micro-batches x 11 accumulation
	steps = 132 sequences. This distributed run preserves that exact global batch.
	Ranks receive 16 or 17 sequences and scale their local mean losses so DDP's
	averaged gradient is the true 132-sequence global mean.

	## Usage

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Banaxi-Tech/custom-optimizer-model-test"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	trust_remote_code=True,
	dtype=torch.bfloat16,
	device_map="auto",
	)
	```

	This is a base model, not an instruction-tuned chat model.