Instructions to use SuperQAI2050/STEM_Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuperQAI2050/STEM_Code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SuperQAI2050/STEM_Code")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SuperQAI2050/STEM_Code", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SuperQAI2050/STEM_Code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SuperQAI2050/STEM_Code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SuperQAI2050/STEM_Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SuperQAI2050/STEM_Code
- SGLang
How to use SuperQAI2050/STEM_Code 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 "SuperQAI2050/STEM_Code" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SuperQAI2050/STEM_Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "SuperQAI2050/STEM_Code" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SuperQAI2050/STEM_Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SuperQAI2050/STEM_Code with Docker Model Runner:
docker model run hf.co/SuperQAI2050/STEM_Code
File size: 1,823 Bytes
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"architectures": [
"NemotronHForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_nemotron_h.NemotronHConfig",
"AutoModel": "modeling_nemotron_h.NemotronHForCausalLM",
"AutoModelForCausalLM": "modeling_nemotron_h.NemotronHForCausalLM"
},
"bos_token_id": 1,
"chunk_size": 128,
"conv_kernel": 4,
"dtype": "bfloat16",
"eos_token_id": 2,
"expand": 2,
"head_dim": 128,
"hidden_dropout": 0.0,
"hidden_size": 2688,
"hybrid_override_pattern": "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME",
"initializer_range": 0.02,
"intermediate_size": 1856,
"layer_norm_epsilon": 1e-05,
"mamba_head_dim": 64,
"mamba_hidden_act": "silu",
"mamba_num_heads": 64,
"mamba_proj_bias": false,
"max_position_embeddings": 262144,
"mlp_bias": false,
"mlp_hidden_act": "relu2",
"model_type": "nemotron_h",
"moe_intermediate_size": 1856,
"moe_shared_expert_intermediate_size": 3712,
"n_group": 1,
"n_groups": 8,
"n_routed_experts": 128,
"n_shared_experts": 1,
"norm_eps": 1e-05,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts_per_tok": 6,
"num_hidden_layers": 52,
"num_key_value_heads": 2,
"num_logits_to_keep": 1,
"pad_token_id": 0,
"partial_rotary_factor": 1.0,
"rescale_prenorm_residual": true,
"residual_in_fp32": false,
"rope_theta": 10000,
"routed_scaling_factor": 2.5,
"sliding_window": null,
"ssm_state_size": 128,
"tie_word_embeddings": false,
"time_step_floor": 0.0001,
"time_step_limit": [
0.0,
Infinity
],
"time_step_max": 0.1,
"time_step_min": 0.001,
"topk_group": 1,
"transformers_version": "4.57.1",
"use_bias": false,
"use_cache": true,
"use_conv_bias": true,
"use_mamba_kernels": true,
"vocab_size": 131072
}
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