Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download configs/nexus_coder_xlarge.yaml from AdminReal/NexusCoder: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/AdminReal/NexusCoder/resolve/main/configs/nexus_coder_xlarge.yaml
- Command line
-
hf download hf://AdminReal/NexusCoder/configs/nexus_coder_xlarge.yaml
-
curl -L -o nexus_coder_xlarge.yaml https://huggingface.co/AdminReal/NexusCoder/resolve/main/configs/nexus_coder_xlarge.yaml
2.04 kB
| # Nexus Coder Configuration - XLarge (~30B/3B) v0.3 | |
| # Research-only. Requires 64+ H100 80GB GPUs. | |
| # Author: Hieu Louis (2026) | |
| model: | |
| name: "Nexus Coder XLarge" | |
| agent_name: "Nexus" | |
| author: "Hieu Louis" | |
| version: "0.3.0-xlarge" | |
| github: "mhieuhonda" | |
| year: "2026" | |
| architecture: | |
| vocab_size: 64000 | |
| hidden_size: 4096 | |
| num_hidden_layers: 24 | |
| num_attention_heads: 32 | |
| num_kv_heads: 8 | |
| head_dim: 128 | |
| intermediate_size: 11264 | |
| hidden_act: "silu" | |
| norm_type: "rmsnorm" | |
| moe: | |
| num_experts: 48 | |
| num_active_experts: 4 | |
| router_aux_loss_coef: 0.001 | |
| context: | |
| max_position_embeddings: 65536 # 64k tokens | |
| rotary_emb_base: 10000.0 | |
| rope_scaling_type: "dynamic" # NTK-aware for 2× context extension | |
| rope_scaling_factor: 2.0 | |
| attention: | |
| use_flash_attention: true | |
| use_flash_attention_2: true # recommended at this scale | |
| use_alibi: false | |
| use_sliding_window: true | |
| sliding_window_size: 8192 | |
| use_qk_norm: true | |
| mlp_parallel: true | |
| compute: | |
| use_kv_cache: true | |
| kv_cache_quantization: "int8" # saves KV cache memory at long context | |
| gradient_checkpointing: true # essential at this scale | |
| tensor_parallel_size: 4 | |
| pipeline_parallel_size: 1 | |
| expert_parallel_size: 4 | |
| sequence_parallel: false | |
| params: | |
| total: "~30B" | |
| active: "~3B" | |
| expert_utilization: "8.3%" | |
| estimated_disk_mb_fp16: 60000 | |
| estimated_disk_mb_int8: 30000 | |
| estimated_disk_mb_int4: 15000 | |
| training: | |
| learning_rate: 2.0e-4 | |
| weight_decay: 0.01 | |
| warmup_steps: 500 | |
| max_steps: 10000 | |
| per_device_batch_size: 1 | |
| gradient_accumulation_steps: 32 | |
| logging_steps: 10 | |
| save_steps: 1000 | |
| max_grad_norm: 1.0 | |
| seed: 42 | |
| use_amp: true | |
| use_deepspeed: true | |
| deepspeed_config: "configs/ds_config_zero3.json" | |
| inference: | |
| max_new_tokens: 500 | |
| temperature: 0.7 | |
| 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: 80 | |
| recommended_gpus: "64+ H100 80GB" | |