Text Generation
Transformers
Safetensors
llada
feature-extraction
code
reasoning
diffusion-language-model
custom_code
Instructions to use nzl-thu/LLaDA-Instruct-JustGRPO-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nzl-thu/LLaDA-Instruct-JustGRPO-Code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nzl-thu/LLaDA-Instruct-JustGRPO-Code", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nzl-thu/LLaDA-Instruct-JustGRPO-Code", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nzl-thu/LLaDA-Instruct-JustGRPO-Code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nzl-thu/LLaDA-Instruct-JustGRPO-Code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nzl-thu/LLaDA-Instruct-JustGRPO-Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-Code
- SGLang
How to use nzl-thu/LLaDA-Instruct-JustGRPO-Code 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 "nzl-thu/LLaDA-Instruct-JustGRPO-Code" \ --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": "nzl-thu/LLaDA-Instruct-JustGRPO-Code", "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 "nzl-thu/LLaDA-Instruct-JustGRPO-Code" \ --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": "nzl-thu/LLaDA-Instruct-JustGRPO-Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nzl-thu/LLaDA-Instruct-JustGRPO-Code with Docker Model Runner:
docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-Code
File size: 1,414 Bytes
54a3660 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | {
"activation_type": "silu",
"alibi": false,
"alibi_bias_max": 8.0,
"architectures": [
"LLaDAModelLM"
],
"attention_dropout": 0.0,
"attention_layer_norm": false,
"attention_layer_norm_with_affine": true,
"auto_map": {
"AutoConfig": "configuration_llada.LLaDAConfig",
"AutoModel": "modeling_llada.LLaDAModelLM",
"AutoModelForCausalLM": "modeling_llada.LLaDAModelLM"
},
"bias_for_layer_norm": false,
"block_group_size": 1,
"block_type": "llama",
"d_model": 4096,
"embedding_dropout": 0.0,
"embedding_size": 126464,
"eos_token_id": 126081,
"flash_attention": false,
"include_bias": false,
"include_qkv_bias": false,
"init_cutoff_factor": null,
"init_device": "meta",
"init_fn": "mitchell",
"init_std": 0.02,
"input_emb_norm": false,
"layer_norm_type": "rms",
"layer_norm_with_affine": true,
"mask_token_id": 126336,
"max_sequence_length": 4096,
"mlp_hidden_size": 12288,
"mlp_ratio": 4,
"model_type": "llada",
"multi_query_attention": null,
"n_heads": 32,
"n_kv_heads": 32,
"n_layers": 32,
"pad_token_id": 126081,
"precision": "amp_bf16",
"residual_dropout": 0.0,
"rms_norm_eps": 1e-05,
"rope": true,
"rope_full_precision": true,
"rope_theta": 500000.0,
"scale_logits": false,
"torch_dtype": "float32",
"transformers_version": "4.51.3",
"use_cache": false,
"vocab_size": 126464,
"weight_tying": false
}
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