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
| { | |
| "package": "bind2_0", | |
| "spec_title": "bind2_0 \u2014 delta-rule backbone with a forced bottleneck", | |
| "generation": "bind2_0", | |
| "buildable": "BLOCKED", | |
| "entries": [ | |
| { | |
| "source": "pilot-4060/train_bind2_0_babylm.py", | |
| "kind": "code", | |
| "sha256": "c325148dbecc8da56faf6f94b9494f1910cd437bc16d4c0b340eccdd279fb0c8", | |
| "bytes": 4138, | |
| "source_commit": "c3d39af06e8ac30ad3d8c930548dc03db4698ffb", | |
| "status": "COPIED", | |
| "dest": "src/train_bind2_0_babylm.py" | |
| }, | |
| { | |
| "source": "pilot-4060/modeling_bind2_0.py", | |
| "kind": "code", | |
| "sha256": "1a950720257d560e02833b4c2e6c5017ee03a7585be6204c1b726930fd80e6ed", | |
| "bytes": 5047, | |
| "source_commit": "c3d39af06e8ac30ad3d8c930548dc03db4698ffb", | |
| "status": "COPIED", | |
| "dest": "src/modeling_bind2_0.py", | |
| "source_sha256": "12ea39f0b958ae7ad304bbf4cc7a71afc984fe37c5805c97151084348a47f899", | |
| "scrubbed": "1 text rule(s); AST verified: structure identical" | |
| }, | |
| { | |
| "source": "alaya-strict-small/tokenizer.json", | |
| "kind": "data", | |
| "sha256": "6582b724cb5196ca72baf2a6b91c17be2631901e93feadce1fcd74c8d5e9089a", | |
| "bytes": 1096025, | |
| "source_commit": "c3d39af06e8ac30ad3d8c930548dc03db4698ffb", | |
| "status": "COPIED", | |
| "dest": "data/tokenizer.json" | |
| }, | |
| { | |
| "source": "alaya-strict-small/tokens_u16.bin", | |
| "kind": "external", | |
| "sha256": "bce1a7681c6dc323ad0166219d63e8af01ae55a8c07d74d9774432d23b440c4e", | |
| "bytes": 32592654, | |
| "source_commit": "(untracked)", | |
| "status": "REFERENCED-TOO-LARGE" | |
| } | |
| ] | |
| } |