Instructions to use SearchSwarm/SearchSwarm-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SearchSwarm/SearchSwarm-30B-A3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SearchSwarm/SearchSwarm-30B-A3B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SearchSwarm/SearchSwarm-30B-A3B") model = AutoModelForCausalLM.from_pretrained("SearchSwarm/SearchSwarm-30B-A3B") 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 SearchSwarm/SearchSwarm-30B-A3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SearchSwarm/SearchSwarm-30B-A3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SearchSwarm/SearchSwarm-30B-A3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SearchSwarm/SearchSwarm-30B-A3B
- SGLang
How to use SearchSwarm/SearchSwarm-30B-A3B 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 "SearchSwarm/SearchSwarm-30B-A3B" \ --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": "SearchSwarm/SearchSwarm-30B-A3B", "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 "SearchSwarm/SearchSwarm-30B-A3B" \ --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": "SearchSwarm/SearchSwarm-30B-A3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SearchSwarm/SearchSwarm-30B-A3B with Docker Model Runner:
docker model run hf.co/SearchSwarm/SearchSwarm-30B-A3B
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| "seq_kd": false, | |
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| "importance_sampling_level": "token", | |
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| "vllm_gpu_memory_utilization": 0.9, | |
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| "sleep_level": 0, | |
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| "global_batch_size": 128, | |
| "recompute_granularity": "selective", | |
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| "num_train_epochs": 3, | |
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| "gradient_accumulation_fusion": false, | |
| "cross_entropy_loss_fusion": true, | |
| "cross_entropy_fusion_impl": "te", | |
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| "attention_backend": "<AttnBackend.flash: 1>", | |
| "optimizer": "adam", | |
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| "main_grads_dtype": "float32", | |
| "main_params_dtype": "float32", | |
| "exp_avg_dtype": "float32", | |
| "exp_avg_sq_dtype": "float32", | |
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| "weight_decay": 0.01, | |
| "weight_decay_incr_style": "constant", | |
| "start_weight_decay": 0.01, | |
| "end_weight_decay": 0.01, | |
| "clip_grad": 1.0, | |
| "adam_beta1": 0.9, | |
| "adam_beta2": 0.95, | |
| "adam_eps": 1e-08, | |
| "sgd_momentum": 0.9, | |
| "muon_momentum": 0.9, | |
| "muon_split_qkv": true, | |
| "muon_use_nesterov": false, | |
| "muon_scale_mode": "spectral", | |
| "muon_fp32_matmul_prec": "medium", | |
| "muon_num_ns_steps": 5, | |
| "muon_tp_mode": "blockwise", | |
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| "output_dir": "/dnn_training_sys/ningpu/megatron_sft/outputs/v7/setting5/v1-20260512-131221", | |
| "save_steps": 100, | |
| "no_save_optim": true, | |
| "no_save_rng": true, | |
| "mcore_model": null, | |
| "mcore_adapter": null, | |
| "no_load_optim": false, | |
| "no_load_rng": false, | |
| "finetune": true, | |
| "perform_initialization": false, | |
| "use_cpu_initialization": false, | |
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| "save_total_limit": null, | |
| "metric_for_best_model": "loss", | |
| "greater_is_better": false, | |
| "use_persistent_ckpt_worker": false, | |
| "dist_ckpt_save_pre_mcore_014": false, | |
| "dist_ckpt_optim_fully_reshardable": false, | |
| "distrib_optim_fully_reshardable_mem_efficient": false, | |
| "local_rank": 0, | |
| "use_distributed_optimizer": true, | |
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| "overlap_p2p_comm": false, | |
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| "sequence_parallel": true, | |
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| "expert_model_parallel_size": 4, | |
| "expert_tensor_parallel_size": 1, | |
| "report_to": [ | |
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| "wandb" | |
| ], | |
| "logging_steps": 1, | |
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| "tensorboard_queue_size": 50, | |
| "wandb_project": "ma-explore", | |
| "wandb_exp_name": "v7_chinese_must_visit_short0_5_sub038", | |
| "swanlab_project": "megatron-swift", | |
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| "eval_iters": -1, | |
| "eval_steps": 100, | |
| "fp8_format": null, | |
| "fp8_recipe": "delayed", | |
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| "fp8_amax_compute_algo": "max", | |
| "fp8_param_gather": false, | |
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| "moe_shared_expert_overlap": true, | |
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| "moe_pad_expert_input_to_capacity": false, | |
| "moe_token_drop_policy": null, | |
| "mtp_num_layers": null, | |
| "mtp_loss_scaling_factor": 0.1, | |
| "mtp_decoder_input_detach": false, | |
| "mtp_shared_weights": false, | |
| "save_safetensors": true, | |
| "ref_model": null, | |
| "ref_adapters": [], | |
| "merge_lora": true, | |
| "max_shard_size": "5GB", | |
| "vit_gradient_checkpointing": false, | |
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| "dsa_indexer_use_sparse_loss": false, | |
| "check_model": true, | |
| "apply_wd_to_qk_layernorm": false, | |
| "linear_decoupled_in_proj": false, | |
| "enable_dft_loss": false, | |
| "enable_channel_loss": false, | |
| "save_strategy": "steps", | |
| "callbacks": [ | |
| "print", | |
| "default_flow", | |
| "tensorboard", | |
| "wandb" | |
| ], | |
| "add_version": true, | |
| "create_checkpoint_symlink": false, | |
| "swift_version": "4.2.0", | |
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| "rank": 0, | |
| "global_world_size": 64, | |
| "local_world_size": 8, | |
| "model_suffix": "Tongyi-DeepResearch-30B-A3B", | |
| "model_info": "ModelInfo(model_type='qwen3_moe', model_dir='/dnn_training_sys/ningpu/megatron_sft/models/Tongyi-DeepResearch-30B-A3B', torch_dtype=torch.bfloat16, max_model_len=131072, quant_method=None, quant_bits=None, rope_scaling=None, is_moe_model=True, is_multimodal=False, config=None, task_type='causal_lm', num_labels=None)", | |
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| "model_dir": "/dnn_training_sys/ningpu/megatron_sft/models/Tongyi-DeepResearch-30B-A3B", | |
| "template_meta": "Qwen3MixedTemplateMeta(template_type='qwen3', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=<class 'swift.template.base.Template'>, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system=None, auto_add_bos=False, stop_words=['<|endoftext|>'], agent_template='hermes', is_thinking=True, thinking_prefix='', non_thinking_prefix='<think>\\n\\n</think>\\n\\n', history_thinking_prefix='')", | |
| "_val_dataset_exists": false, | |
| "hub": "<class 'swift.hub.hub.HFHub'>", | |
| "seq_length": 131072, | |
| "is_multimodal": false, | |
| "megatron_model_meta": "ModelMeta(model_type='gpt', model_types=['qwen2', 'llama', 'qwen3', 'qwen2_moe', 'qwen3_moe', 'internlm3', 'mimo', 'deepseek', 'deepseek_v2', 'deepseek_v3', 'deepseek_v32', 'kimi_k2', 'dots1', 'ernie4_5', 'ernie4_5_moe', 'glm4_moe', 'glm4_moe_lite', 'glm_moe_dsa', 'gpt_oss'], bridge_cls=<class 'mcore_bridge.bridge.gpt_bridge.GPTBridge'>, visual_cls=None, is_multimodal=False, loader=<class 'mcore_bridge.model.register.ModelLoader'>)", | |
| "fp8": null, | |
| "world_size": 64, | |
| "data_parallel_size": 4, | |
| "num_microbatches": 32 | |
| } |