Text Generation
Transformers
Safetensors
English
slmoe
causal-lm
base-model
mixture-of-experts
sequence-routing
custom-code
trust-remote-code
custom_code
Instructions to use Banaxi-Tech/slmoe-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Banaxi-Tech/slmoe-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Banaxi-Tech/slmoe-test", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Banaxi-Tech/slmoe-test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Banaxi-Tech/slmoe-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Banaxi-Tech/slmoe-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Banaxi-Tech/slmoe-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Banaxi-Tech/slmoe-test
- SGLang
How to use Banaxi-Tech/slmoe-test 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 "Banaxi-Tech/slmoe-test" \ --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": "Banaxi-Tech/slmoe-test", "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 "Banaxi-Tech/slmoe-test" \ --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": "Banaxi-Tech/slmoe-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Banaxi-Tech/slmoe-test with Docker Model Runner:
docker model run hf.co/Banaxi-Tech/slmoe-test
| { | |
| "parameters": 25449472, | |
| "active_parameters": 7902208, | |
| "architecture": { | |
| "vocab_size": 8192, | |
| "hidden_size": 256, | |
| "num_hidden_layers": 8, | |
| "num_attention_heads": 8, | |
| "num_key_value_heads": 2, | |
| "head_dim": 32, | |
| "num_experts": 64, | |
| "num_experts_per_sequence": 13, | |
| "expert_intermediate_size": 56, | |
| "router_prefix_length": 32, | |
| "router_jitter_noise": 0.01, | |
| "router_aux_loss_coeff": 0.01, | |
| "router_z_loss_coeff": 0.001, | |
| "expert_output_scale": 3.605551275463989, | |
| "max_position_embeddings": 4096, | |
| "rope_theta": 100000.0, | |
| "rms_norm_eps": 1e-06, | |
| "initializer_range": 0.02, | |
| "use_cache": true, | |
| "transformers_version": "5.15.0", | |
| "architectures": null, | |
| "output_hidden_states": false, | |
| "return_dict": true, | |
| "dtype": null, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "problem_type": null, | |
| "_name_or_path": "", | |
| "tie_word_embeddings": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "pad_token_id": 0, | |
| "unk_token_id": 3, | |
| "model_type": "slmoe", | |
| "output_attentions": false | |
| }, | |
| "training_percent": 100, | |
| "step": 28610, | |
| "total_steps": 28610, | |
| "tokens_seen": 59999518720, | |
| "target_tokens": 60000000000, | |
| "trained_token_target": 59999518720, | |
| "tokens_per_step": 2097152, | |
| "tokens_per_micro_batch": 1048576, | |
| "local_batch": 64, | |
| "effective_global_batch": 512, | |
| "gradient_accumulation_steps": 2, | |
| "world_size": 4, | |
| "gpu_name": "NVIDIA H200", | |
| "optimizer": "AdamW", | |
| "peak_lr": 0.003, | |
| "architecture_revision": "bd0ade7689958997e1889c641a782a69c0bea918", | |
| "tokenizer_repo": "BananaMind/BananaMind-2-Mini", | |
| "tokenizer_revision": "6400d0a6dcbe52f3c725291cb97c079c591c05a4", | |
| "dataset_revisions": { | |
| "fineweb_hq": "e58199cdd52438d94405df1a4d8630cc5f13bf84", | |
| "fineweb_edu": "87f09149ef4734204d70ed1d046ddc9ca3f2b8f9", | |
| "dclm": "a3b142c183aebe5af344955ae20836eb34dcf69b", | |
| "cosmopedia_v2": "3ba9d605774198c5868892d7a8deda78031a781f", | |
| "finemath": "e92b25a616738fe95dc186b64dfb19f9c8525594", | |
| "npset2": "222b911cd9119624dd65ab084d69542f6e04631e" | |
| }, | |
| "source_tokens": { | |
| "fineweb_hq": 16919822336, | |
| "fineweb_edu": 16949182464, | |
| "dclm": 9330229248, | |
| "cosmopedia_v2": 7499415552, | |
| "finemath": 7470055424, | |
| "npset2": 1830813696 | |
| }, | |
| "elapsed_seconds": 15931.297726869583 | |
| } | |