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 Settings
- 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
| { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_llama.LlamaConfig", | |
| "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM" | |
| }, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 48, | |
| "eos_token_id": 48, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 6144, | |
| "initializer_range": 0.005, | |
| "intermediate_size": 21504, | |
| "max_position_embeddings": 65536, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 48, | |
| "num_hidden_layers": 60, | |
| "num_key_value_heads": 8, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 3e-06, | |
| "rope_scaling": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 10.0, | |
| "original_max_position_embeddings": 8192, | |
| "rope_type": "yarn", | |
| "type": "yarn", | |
| "truncate": true | |
| }, | |
| "rope_theta": 200000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.46.3", | |
| "use_cache": true, | |
| "vocab_size": 131072 | |
| } | |