Text Generation
Transformers
Safetensors
PyTorch
English
bananaall
causal-lm
language-model
base-model
small-language-model
bananamind
bananamind2
ternary
int8-embeddings
digit-tokenizer
custom-code
trust-remote-code
custom-architecture
custom_code
8-bit precision
Instructions to use BananaMind/TernaryBananaMind-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# 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
Download tokenizer_config.json from BananaMind/TernaryBananaMind-10M: direct link, hf CLI and curl.
- Browser
- Download file 295 Bytes
-
https://huggingface.co/BananaMind/TernaryBananaMind-10M/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://BananaMind/TernaryBananaMind-10M/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/BananaMind/TernaryBananaMind-10M/resolve/main/tokenizer_config.json
295 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|bos|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|eos|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 4096, | |
| "pad_token": "<|pad|>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<|unk|>" | |
| } | |