Instructions to use BananaMind/TernaryBananaMind-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BananaMind/TernaryBananaMind-10M with Transformers:

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

pipe = pipeline("text-generation", model="BananaMind/TernaryBananaMind-10M", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("BananaMind/TernaryBananaMind-10M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BananaMind/TernaryBananaMind-10M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BananaMind/TernaryBananaMind-10M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BananaMind/TernaryBananaMind-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BananaMind/TernaryBananaMind-10M

SGLang

How to use BananaMind/TernaryBananaMind-10M 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 "BananaMind/TernaryBananaMind-10M" \
    --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": "BananaMind/TernaryBananaMind-10M",
		"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 "BananaMind/TernaryBananaMind-10M" \
        --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": "BananaMind/TernaryBananaMind-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BananaMind/TernaryBananaMind-10M with Docker Model Runner:
```
docker model run hf.co/BananaMind/TernaryBananaMind-10M
```

TernaryBananaMind-10M

File size: 802 Bytes

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{
  "architecture_style": "bananamind2",
  "architectures": [
    "BananaAllForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_bananaall.BananaAllConfig",
    "AutoModelForCausalLM": "modeling_bananaall_int8_v2.BananaAllForCausalLM"
  },
  "bos_token_id": 1,
  "dtype": "float32",
  "eos_token_id": 2,
  "head_dim": 64,
  "hidden_size": 256,
  "intermediate_size": 704,
  "lft": false,
  "max_position_embeddings": 4096,
  "model_type": "bananaall",
  "num_attention_heads": 4,
  "num_hidden_layers": 10,
  "num_key_value_heads": 2,
  "pad_token_id": 0,
  "rms_norm_eps": 1e-06,
  "rope_theta": 100000.0,
  "ternary": true,
  "tie_word_embeddings": false,
  "transformers_version": "5.14.1",
  "use_cache": false,
  "vocab_size": 2048,
  "packed_ternary": true,
  "embedding_bits": 8
}