Text Generation
Transformers
Safetensors
English
babylm
babylm-2026
strict-small
linear-attention
state-tracking
delta-rule
custom_code
Instructions to use SecludedCorner/bind2_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SecludedCorner/bind2_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SecludedCorner/bind2_0", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SecludedCorner/bind2_0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SecludedCorner/bind2_0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SecludedCorner/bind2_0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SecludedCorner/bind2_0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SecludedCorner/bind2_0
- SGLang
How to use SecludedCorner/bind2_0 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 "SecludedCorner/bind2_0" \ --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": "SecludedCorner/bind2_0", "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 "SecludedCorner/bind2_0" \ --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": "SecludedCorner/bind2_0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SecludedCorner/bind2_0 with Docker Model Runner:
docker model run hf.co/SecludedCorner/bind2_0
| # Exact dependency versions the bind2_0 checkpoints were trained/exported with. | |
| # Source: local conda env `babylm-smoke` (C:/Users/yulin/.conda/envs/babylm-smoke), | |
| # queried via pip on 2026-07-15. | |
| # | |
| # Python: 3.11.15 | |
| # | |
| # NOTE on fla (flash-linear-attention): the inlined HF modeling code | |
| # (modeling_babylm.py) DOES import it — lazily, inside GDNBlock: | |
| # `from fla.layers import GatedDeltaNet` (only executed when arch == "bind2_0") | |
| # Since the bind2_0 exports instantiate GDN blocks, fla IS required at runtime | |
| # to load/run these checkpoints. Installed from PyPI as release 0.5.1 | |
| # (no commit-pin / direct-URL metadata present in the env; pulls fla-core 0.5.1). | |
| # | |
| # NOTE on triton: the env uses the Windows fork `triton-windows` | |
| # (github.com/woct0rdho/triton-windows); on Linux use the matching upstream | |
| # `triton` that your torch build requires. | |
| # torch build is CUDA 12.6 (`+cu126`); pick the equivalent build for your platform. | |
| torch==2.12.1+cu126 | |
| transformers==5.13.0 | |
| triton-windows==3.7.1.post27 | |
| flash-linear-attention==0.5.1 | |
| fla-core==0.5.1 | |
| safetensors==0.8.0 | |
| numpy==2.4.6 | |