Instructions to use albertge/llada-8b-dllm-registers-code64-codetags-t4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use albertge/llada-8b-dllm-registers-code64-codetags-t4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use albertge/llada-8b-dllm-registers-code64-codetags-t4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "albertge/llada-8b-dllm-registers-code64-codetags-t4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "albertge/llada-8b-dllm-registers-code64-codetags-t4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/albertge/llada-8b-dllm-registers-code64-codetags-t4

SGLang

How to use albertge/llada-8b-dllm-registers-code64-codetags-t4 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 "albertge/llada-8b-dllm-registers-code64-codetags-t4" \
    --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": "albertge/llada-8b-dllm-registers-code64-codetags-t4",
		"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 "albertge/llada-8b-dllm-registers-code64-codetags-t4" \
        --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": "albertge/llada-8b-dllm-registers-code64-codetags-t4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use albertge/llada-8b-dllm-registers-code64-codetags-t4 with Docker Model Runner:
```
docker model run hf.co/albertge/llada-8b-dllm-registers-code64-codetags-t4
```

llada-8b-dllm-registers-code64-codetags-t4

Continued chunked SFT from albertge/llada-8b-dllm-registers-mix60k-t4 on the code-only split of mix60k at C=64 with -delimited targets.


This checkpoint is part of the dLLM Registers project — register tokens as a bounded, trained, continuous channel for carrying decoding state across denoising windows in diffusion language models.

Paper section: tab:code_chunk64 (Markovian Thinking arm)
Carry channel: discrete Markovian-Thinking tail (channel_mode=tail, num_registers=0, tail_length=4)
Base model: GSAI-ML/LLaDA-8B-Base
Training data: OpenCodeInstruct subset of mix60k (~30K traces), ... targets
Training config: SFT/amlt/chunked_sft_mix60k_code64_code_tags_continuation_bonete54.yaml
Chunk size: C = 64 tokens
Prompt dropout rate (Bernoulli per-trace CSG mask): 0.0
Date uploaded: 2026-06-13


	
		
	
	
		How to load
	

from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True)

To use the carry channel correctly at inference, see the evaluator at
eval/eval.py
and the wrapper eval/run_mix60k_full_eval.sh.
Key flags for this checkpoint:
--num_registers 0 --channel_mode tail --tail_length 4

	
		
	
	
		Repository
	

Training and eval code: https://github.com/lbertge/d1-registers

	
		
	
	
		Citation
	

If you use this checkpoint, please cite the dLLM Registers paper:
@misc{dllm-registers-2026,
  title  = {Register Tokens for Unbounded Reasoning in Diffusion Language Models},
  author = {Albert Ge and collaborators},
  year   = {2026},
  note   = {Preprint},
  url    = {https://github.com/lbertge/d1-registers}
}

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for albertge/llada-8b-dllm-registers-code64-codetags-t4

Base model

GSAI-ML/LLaDA-8B-Base

Finetuned

(17)

this model