Instructions to use OpenResearcher/OpenResearcher-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenResearcher/OpenResearcher-30B-A3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenResearcher/OpenResearcher-30B-A3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenResearcher/OpenResearcher-30B-A3B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OpenResearcher/OpenResearcher-30B-A3B", trust_remote_code=True)
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

vLLM

How to use OpenResearcher/OpenResearcher-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenResearcher/OpenResearcher-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": "OpenResearcher/OpenResearcher-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenResearcher/OpenResearcher-30B-A3B

SGLang

How to use OpenResearcher/OpenResearcher-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 "OpenResearcher/OpenResearcher-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": "OpenResearcher/OpenResearcher-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 "OpenResearcher/OpenResearcher-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": "OpenResearcher/OpenResearcher-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenResearcher/OpenResearcher-30B-A3B with Docker Model Runner:
```
docker model run hf.co/OpenResearcher/OpenResearcher-30B-A3B
```

OpenResearcher-30B-A3B / config.json

ZhuofengLi

Upload folder using huggingface_hub

f8b22df verified 4 months ago

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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,
	"eos_token_id": 2,
	"expand": 2,
	"head_dim": 128,
	"hidden_dropout": 0.0,
	"hidden_size": 2688,
	"hybrid_override_pattern": "MEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEME",
	"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,
	"mamba_ssm_cache_dtype": "float32",
	"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_max": 0.1,
	"time_step_min": 0.001,
	"topk_group": 1,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.55.4",
	"use_bias": false,
	"use_cache": true,
	"use_conv_bias": true,
	"use_mamba_kernels": true,
	"vocab_size": 131072
	}