Text Generation
Transformers
Safetensors
English
deepseek_v3
Mixture of Experts
mixture-of-experts
gravity
txgravity
therapeutics
tdc
trillion-labs
conversational
text-generation-inference
Instructions to use trillionlabs/TxGravity-30B-A5B-Open with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trillionlabs/TxGravity-30B-A5B-Open with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trillionlabs/TxGravity-30B-A5B-Open") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open") model = AutoModelForCausalLM.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use trillionlabs/TxGravity-30B-A5B-Open with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trillionlabs/TxGravity-30B-A5B-Open" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trillionlabs/TxGravity-30B-A5B-Open", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open
- SGLang
How to use trillionlabs/TxGravity-30B-A5B-Open 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 "trillionlabs/TxGravity-30B-A5B-Open" \ --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": "trillionlabs/TxGravity-30B-A5B-Open", "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 "trillionlabs/TxGravity-30B-A5B-Open" \ --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": "trillionlabs/TxGravity-30B-A5B-Open", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trillionlabs/TxGravity-30B-A5B-Open with Docker Model Runner:
docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open
Download config.json from trillionlabs/TxGravity-30B-A5B-Open: direct link, hf CLI and curl.
- Browser
- Download file 916 Bytes
-
https://huggingface.co/trillionlabs/TxGravity-30B-A5B-Open/resolve/main/config.json
- Command line
-
hf download hf://trillionlabs/TxGravity-30B-A5B-Open/config.json
-
curl -L -o config.json https://huggingface.co/trillionlabs/TxGravity-30B-A5B-Open/resolve/main/config.json
916 Bytes
| { | |
| "architectures": [ | |
| "DeepseekV3ForCausalLM" | |
| ], | |
| "model_type": "deepseek_v3", | |
| "vocab_size": 151552, | |
| "hidden_size": 2048, | |
| "intermediate_size": 8192, | |
| "moe_intermediate_size": 1408, | |
| "num_hidden_layers": 52, | |
| "num_attention_heads": 16, | |
| "num_key_value_heads": 16, | |
| "q_lora_rank": null, | |
| "kv_lora_rank": 512, | |
| "qk_nope_head_dim": 128, | |
| "qk_rope_head_dim": 64, | |
| "v_head_dim": 128, | |
| "n_routed_experts": 64, | |
| "n_shared_experts": 1, | |
| "num_experts_per_tok": 8, | |
| "first_k_dense_replace": 2, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 1000000.0, | |
| "max_position_embeddings": 131072, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "hidden_act": "silu", | |
| "scoring_func": "sigmoid", | |
| "norm_topk_prob": true, | |
| "routed_scaling_factor": 2.446, | |
| "aux_loss_alpha": 1e-05, | |
| "topk_method": "noaux_tc", | |
| "n_group": 1, | |
| "topk_group": 1, | |
| "eos_token_id": 151336, | |
| "pad_token_id": 151329 | |
| } |