Text Generation
Transformers
Safetensors
English
looped_transformer
tiny
tiny-lm
tiny-model
slm
SLM
small-language-model
from-scratch
looped-transformer
weight-sharing
Instructions to use Compactbot/bananamind-3-2.5m-lft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Compactbot/bananamind-3-2.5m-lft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Compactbot/bananamind-3-2.5m-lft")# pip install -U transformers accelerate # Load model directly from transformers import LFT model = LFT.from_pretrained("Compactbot/bananamind-3-2.5m-lft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Compactbot/bananamind-3-2.5m-lft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Compactbot/bananamind-3-2.5m-lft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Compactbot/bananamind-3-2.5m-lft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Compactbot/bananamind-3-2.5m-lft
- SGLang
How to use Compactbot/bananamind-3-2.5m-lft 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 "Compactbot/bananamind-3-2.5m-lft" \ --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": "Compactbot/bananamind-3-2.5m-lft", "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 "Compactbot/bananamind-3-2.5m-lft" \ --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": "Compactbot/bananamind-3-2.5m-lft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Compactbot/bananamind-3-2.5m-lft with Docker Model Runner:
docker model run hf.co/Compactbot/bananamind-3-2.5m-lft
Commit History
-
Add config (#4)
- Add config (d84b2dc985dc4f5876a30a959844696c5b1dbb10)082357f -
Add BPE tokenizer (vocab 12288) (#3)
- Add BPE tokenizer (vocab 12288) (4e10e0998e841445c19bd159ce1b3f4c6ea86804)4ab7c0a -
Add model weights (2,520,704 params, 56 F32 tensors) (#2)
- Add model weights (2,520,704 params, 56 F32 tensors) (fbd063a20c4247847e9d088f5e450191a5bae7d9)039b4ae -
Add model card (honest: surface-grammatical but semantically incoherent at 2.5M; evals within noise of chance) (#1)
- Add model card (honest: surface-grammatical but semantically incoherent at 2.5M; evals within noise of chance) (74435c1fe8a23725e66ecf714f83e9d33e5d7c6f)272f815 -
initial commit
Verified signatureae3d7e3