Text Generation
Transformers
Safetensors
cola2
feature-extraction
custom_code
diffusion
continuous-latent
base-model
conversational
Instructions to use ByteDance-Seed/Cola2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ByteDance-Seed/Cola2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ByteDance-Seed/Cola2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ByteDance-Seed/Cola2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ByteDance-Seed/Cola2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ByteDance-Seed/Cola2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Cola2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ByteDance-Seed/Cola2
- SGLang
How to use ByteDance-Seed/Cola2 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 "ByteDance-Seed/Cola2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Cola2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ByteDance-Seed/Cola2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Cola2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ByteDance-Seed/Cola2 with Docker Model Runner:
docker model run hf.co/ByteDance-Seed/Cola2
Download eval_data/manifest.json from ByteDance-Seed/Cola2: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/ByteDance-Seed/Cola2/resolve/main/eval_data/manifest.json
- Command line
-
hf download hf://ByteDance-Seed/Cola2/eval_data/manifest.json
-
curl -L -o manifest.json https://huggingface.co/ByteDance-Seed/Cola2/resolve/main/eval_data/manifest.json
1.37 kB
| { | |
| "format": "cola2-generate-task-jsonl-v1", | |
| "tasks": { | |
| "arc_easy": { | |
| "file": "arc_easy.jsonl", | |
| "samples": 2375, | |
| "sha256": "82ca2652c55e896130fa0ca4adedb5ce404d8bb16d118b4b1b382bd3907c4024" | |
| }, | |
| "hellaswag": { | |
| "file": "hellaswag.jsonl", | |
| "samples": 10042, | |
| "sha256": "e45c5a826dccfaa2f6f4b3db84ac121e33e3f71703f43b18b38b58d6325db001" | |
| }, | |
| "lambada": { | |
| "file": "lambada.jsonl", | |
| "samples": 5153, | |
| "sha256": "12006b30c189194ddcedfc6ad11c57a7cd107a97340b85199e9fe1766dae7f03" | |
| }, | |
| "mmlu": { | |
| "file": "mmlu.jsonl", | |
| "samples": 14042, | |
| "sha256": "ce429dd9fdc44d56f2299b63cc934df1d88b8cdb7709c72ee24d8323aba95fdd" | |
| }, | |
| "narrativeqa": { | |
| "file": "narrativeqa.jsonl", | |
| "samples": 1000, | |
| "sha256": "28daf47f744fc956892402e62a6ac9c41ca4babc8edf5f7105cfb6bb0eba5ff8" | |
| }, | |
| "race": { | |
| "file": "race.jsonl", | |
| "samples": 4887, | |
| "sha256": "1d742781cac6f9237a33b49654815e119ac6f845ef7613ad9b9e0860b6596c0d" | |
| }, | |
| "story_cloze": { | |
| "file": "story_cloze.jsonl", | |
| "samples": 468, | |
| "sha256": "d6555a855ce1a23f23ac2474a1e9dfda7239ec368b6a5962434db5c2f678c014" | |
| }, | |
| "truthfulqa": { | |
| "file": "truthfulqa.jsonl", | |
| "samples": 817, | |
| "sha256": "ccef0b9ecae13cc5b83c24430c6cfee37d3872455b428c4630bae434a27ec935" | |
| } | |
| } | |
| } | |