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

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
```

List-3.0-Ultra-Coder-Brain / config.json

List-cloud

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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": [
	128,
	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"
	}