Text Generation
Transformers
Safetensors
bailing_hybrid
conversational
custom_code
Eval Results
compressed-tensors
Instructions to use inclusionAI/Ling-2.6-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ling-2.6-1T with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ling-2.6-1T", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-2.6-1T", trust_remote_code=True, dtype="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use inclusionAI/Ling-2.6-1T with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ling-2.6-1T" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/Ling-2.6-1T", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ling-2.6-1T
- SGLang
How to use inclusionAI/Ling-2.6-1T 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 "inclusionAI/Ling-2.6-1T" \ --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": "inclusionAI/Ling-2.6-1T", "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 "inclusionAI/Ling-2.6-1T" \ --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": "inclusionAI/Ling-2.6-1T", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/Ling-2.6-1T with Docker Model Runner:
docker model run hf.co/inclusionAI/Ling-2.6-1T
Add community evaluation results for SWE-BENCH_VERIFIED
#4
by nielsr HF Staff - opened
This PR adds community-provided evaluation results for the following benchmarks:
These results were extracted from the model card. This is based on the new evaluation results feature.
Note: This is an automated PR. Please review the evaluation results before merging.
LGTM
RichardBian changed pull request status to merged
Thanks! Feel free to try it out yourself for the next release :)
We love new features!