Text Generation
Transformers
Safetensors
PyTorch
English
bananamind2_pro
causal-lm
language-model
base-model
small-language-model
bananamind
bananamind2
bananamind2-pro
pretrained
100b-tokens
digit-tokenizer
custom-code
trust-remote-code
custom-architecture
custom_code
Instructions to use SLM-Archive/BananaMind-2-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLM-Archive/BananaMind-2-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SLM-Archive/BananaMind-2-Pro", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SLM-Archive/BananaMind-2-Pro", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SLM-Archive/BananaMind-2-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SLM-Archive/BananaMind-2-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SLM-Archive/BananaMind-2-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SLM-Archive/BananaMind-2-Pro
- SGLang
How to use SLM-Archive/BananaMind-2-Pro 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 "SLM-Archive/BananaMind-2-Pro" \ --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": "SLM-Archive/BananaMind-2-Pro", "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 "SLM-Archive/BananaMind-2-Pro" \ --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": "SLM-Archive/BananaMind-2-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SLM-Archive/BananaMind-2-Pro with Docker Model Runner:
docker model run hf.co/SLM-Archive/BananaMind-2-Pro
Download base_bench_progression.png from SLM-Archive/BananaMind-2-Pro: direct link, hf CLI and curl.
- Browser
- Download file 403 kB
-
https://huggingface.co/SLM-Archive/BananaMind-2-Pro/resolve/main/base_bench_progression.png
- Command line
-
hf download hf://SLM-Archive/BananaMind-2-Pro/base_bench_progression.png
-
curl -L -o base_bench_progression.png https://huggingface.co/SLM-Archive/BananaMind-2-Pro/resolve/main/base_bench_progression.png
403 kB

- Xet hash:
- 1f60fbe6749d7afb4602257de08f3d12d1ab4eeb4c82cd7b8cfc2dab5c46581f
- Size of remote file:
- 403 kB
- SHA256:
- 1c29e557c27ce94b8a1c1d68bc400c3877b674cf966ee16b103b6fa51c768426
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