Text Generation
Transformers
Safetensors
English
list_ultra_coder
code
list-coder
228B
ultra-reasoning
list-ultra
enterprise
mixture-of-experts
Mixture of Experts
mtp
fp8
conversational
custom_code
Instructions to use List-cloud/List-3.0-Ultra-Coder-Brain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use List-cloud/List-3.0-Ultra-Coder-Brain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="List-cloud/List-3.0-Ultra-Coder-Brain", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("List-cloud/List-3.0-Ultra-Coder-Brain", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use List-cloud/List-3.0-Ultra-Coder-Brain with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "List-cloud/List-3.0-Ultra-Coder-Brain" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "List-cloud/List-3.0-Ultra-Coder-Brain", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/List-cloud/List-3.0-Ultra-Coder-Brain
- SGLang
How to use List-cloud/List-3.0-Ultra-Coder-Brain 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 "List-cloud/List-3.0-Ultra-Coder-Brain" \ --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": "List-cloud/List-3.0-Ultra-Coder-Brain", "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 "List-cloud/List-3.0-Ultra-Coder-Brain" \ --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": "List-cloud/List-3.0-Ultra-Coder-Brain", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use List-cloud/List-3.0-Ultra-Coder-Brain with Docker Model Runner:
docker model run hf.co/List-cloud/List-3.0-Ultra-Coder-Brain
File size: 1,870 Bytes
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"model_name": "List-3.0-Ultra-Coder",
"architectures": [
"MiniMaxM2ForCausalLM"
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"auto_map": {
"AutoConfig": "configuration_list_ultra.MiniMaxM2Config",
"AutoModelForCausalLM": "modeling_list_ultra.MiniMaxM2ForCausalLM"
},
"dtype": "bfloat16",
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"intermediate_size": 1536,
"max_position_embeddings": 204800,
"model_type": "list_ultra_coder",
"mtp_transformer_layers": 1,
"num_attention_heads": 48,
"num_experts_per_tok": 8,
"num_hidden_layers": 62,
"num_key_value_heads": 8,
"num_local_experts": 256,
"num_mtp_modules": 3,
"qk_norm_type": "per_layer",
"quantization_config": {
"activation_scheme": "dynamic",
"fmt": "float8_e4m3fn",
"quant_method": "fp8",
"weight_block_size": [
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128
],
"modules_to_not_convert": [
"gate",
"e_score_correction_bias",
"lm_head"
]
},
"rms_norm_eps": 1e-06,
"rope_theta": 5000000,
"rotary_dim": 64,
"scoring_func": "sigmoid",
"shared_intermediate_size": 0,
"tie_word_embeddings": false,
"transformers_version": "4.46.1",
"use_cache": true,
"use_mtp": true,
"use_qk_norm": true,
"use_routing_bias": true,
"vocab_size": 200064,
"model_creator": "List Cloud"
}
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