Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdminReal/NexusCoder with Transformers:

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

pipe = pipeline("text-generation", model="AdminReal/NexusCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder")
model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdminReal/NexusCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdminReal/NexusCoder

SGLang

How to use AdminReal/NexusCoder 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 "AdminReal/NexusCoder" \
    --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": "AdminReal/NexusCoder",
		"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 "AdminReal/NexusCoder" \
        --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": "AdminReal/NexusCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
```
docker model run hf.co/AdminReal/NexusCoder
```

NexusCoder

File size: 1,475 Bytes

eca5751

# Nexus Coder Configuration - Medium version v0.3
# ~1B params, pretrain on 4-8 GPU
# Author: Hieu Louis (2026)

model:
  name: "Nexus Coder Medium"
  agent_name: "Nexus"
  author: "Hieu Louis"
  version: "0.3.0-medium"
  github: "mhieuhonda"
  year: "2026"

architecture:
  vocab_size: 32000
  hidden_size: 1536
  num_hidden_layers: 24
  num_attention_heads: 16
  num_kv_heads: 4
  head_dim: 96
  intermediate_size: 4096
  hidden_act: "silu"
  norm_type: "rmsnorm"

moe:
  num_experts: 16
  num_active_experts: 2
  router_aux_loss_coef: 0.001

context:
  max_position_embeddings: 16384
  rotary_emb_base: 10000.0

attention:
  use_flash_attention: true
  use_flash_attention_2: false
  use_alibi: false
  use_sliding_window: true
  sliding_window_size: 2048
  use_qk_norm: true
  mlp_parallel: true

compute:
  use_kv_cache: true
  kv_cache_quantization: null
  gradient_checkpointing: false

params:
  total: "~1.1B"
  active: "~250M"
  expert_utilization: "12.5%"

training:
  learning_rate: 3.0e-4
  weight_decay: 0.01
  warmup_steps: 100
  max_steps: 5000
  per_device_batch_size: 4
  gradient_accumulation_steps: 4
  logging_steps: 10
  save_steps: 500
  max_grad_norm: 1.0
  seed: 42
  use_amp: true

inference:
  max_new_tokens: 200
  temperature: 0.8
  top_k: 50
  top_p: 0.9
  do_sample: true

personality:
  type: "humorous"
  language: "bilingual"

environment:
  python_version: "3.12.13"
  pytorch_version: ">=2.0"
  cuda_required: true
  min_gpu_memory_gb: 16