Instructions to use TildeAI/TildeOpen-30b-64k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TildeAI/TildeOpen-30b-64k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TildeAI/TildeOpen-30b-64k", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TildeAI/TildeOpen-30b-64k", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("TildeAI/TildeOpen-30b-64k", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TildeAI/TildeOpen-30b-64k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TildeAI/TildeOpen-30b-64k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TildeAI/TildeOpen-30b-64k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TildeAI/TildeOpen-30b-64k
- SGLang
How to use TildeAI/TildeOpen-30b-64k 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 "TildeAI/TildeOpen-30b-64k" \ --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": "TildeAI/TildeOpen-30b-64k", "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 "TildeAI/TildeOpen-30b-64k" \ --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": "TildeAI/TildeOpen-30b-64k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TildeAI/TildeOpen-30b-64k with Docker Model Runner:
docker model run hf.co/TildeAI/TildeOpen-30b-64k
| from packaging import version | |
| import transformers | |
| from transformers import LlamaForCausalLM as HFLlamaForCausalLM | |
| import warnings | |
| _TRANSFORMERS_VERSION = version.parse(transformers.__version__) | |
| print(f"[llama-yarn] Detected transformers version: {_TRANSFORMERS_VERSION}") | |
| if _TRANSFORMERS_VERSION >= version.parse("5.0.0"): | |
| _patch_version = _TRANSFORMERS_VERSION | |
| print( | |
| f"[llama-yarn] Using default transformers implementation, " | |
| f"since transformers version {_patch_version} >= 5.0.0" | |
| ) | |
| LlamaForCausalLM = HFLlamaForCausalLM | |
| else: | |
| _patch_version = version.parse("4.46.3") | |
| print(f"[llama-yarn] Using transformers<5 patch (target version {_patch_version})") | |
| from .llama_yarn_patch_4x import LlamaForCausalLMYarn4x as LlamaForCausalLM | |
| if _TRANSFORMERS_VERSION == _patch_version: | |
| print( | |
| f"[llama-yarn] Patch version matches transformers exactly " | |
| f"({_TRANSFORMERS_VERSION})" | |
| ) | |
| else: | |
| warnings.warn( | |
| "[llama-yarn] Patch version mismatch:\n" | |
| f" transformers installed: {_TRANSFORMERS_VERSION}\n" | |
| f" patch built for: {_patch_version}\n" | |
| "The model may still work but compatibility is not guaranteed.", | |
| RuntimeWarning, | |
| ) | |