pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl

Pearl-certified variant of Qwen3-30B-A3B-Instruct, intended to run with the Pearl vLLM mining plugin.

• Project website: https://pearlresearch.ai
• Pearl repository: https://github.com/pearl-research-labs/pearl
• Miner docs: https://github.com/pearl-research-labs/pearl/tree/master/miner

Model Details

• Base model: Qwen/Qwen3-30B-A3B-Instruct-2507 (Mixture-of-Experts)
• Architecture: Qwen3MoeForCausalLM
• Total parameters: 30B (3B active per token, 8 experts routed per token)
• Hidden layers: 48
• Quantization: Mixed precision (W7A7 + FP8) via Pearl quantization.
• Model type: Causal LLM (instruction-tuned, MoE)
• Primary runtime target: Pearl vLLM plugin (miner/vllm-miner)
• Intended use: Text generation with Pearl mining integration

Evaluation

MMLU-Pro (full test split, 12,032 questions), generative 5-shot CoT, run with the Qwen3-30B-A3B-Instruct-2507 sampling recipe (temperature=0.7, top_p=0.8, top_k=20, min_p=0) via lm-eval-harness on vLLM.

Model	MMLU-Pro
Original	77.88%
Pearl	77.33%

Intended Use

This model is intended to be served through the Pearl miner stack, where vLLM inference is integrated with Pearl mining workflows.

Typical flow:

1. Run pearld with RPC enabled.
2. Start the Pearl miner/vLLM stack.
3. Serve this model through vLLM while Pearl gateway/miner components handle mining-side integration.

How To Use (Pearl vLLM Plugin)

Follow the miner setup from the Pearl repo:

• pearl/miner README

High-level prerequisites:

• Python 3.12
• uv
• CUDA + NVIDIA GPU (sm90 class, e.g. H100/H200, per project docs)
• Rust toolchain
• Running pearld node with RPC credentials

Docker Example

From the Pearl repository root:

docker buildx build -t vllm_miner . -f miner/vllm-miner/Dockerfile

docker run --rm -it --gpus all \
  -p 8000:8000 -p 8337:8337 -p 8339:8339 \
  -e PEARLD_RPC_URL=<PEARLD_URL> \
  -e PEARLD_RPC_USER=<RPC_USER> \
  -e PEARLD_RPC_PASSWORD=<RPC_PASSWORD> \
  -e VLLM_USE_DEEP_GEMM=0 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --shm-size 8g \
  vllm_miner:latest \
  pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl \
  --host 0.0.0.0 --port 8000 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9 \
  --enforce-eager

vLLM Standalone

pip install vllm

vllm serve "pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl"

curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl",
    "messages": [
      {
        "role": "user",
        "content": "What is the capital of France?"
      }
    ]
  }'

Transformers

from transformers import pipeline

pipe = pipeline("text-generation", model="pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl")
model = AutoModelForCausalLM.from_pretrained("pearl-ai/Qwen3-30B-A3B-Instruct-2507-pearl")

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]:]))

License

This model is based on Qwen3 and is distributed under the Apache 2.0 license.

Limitations

• Can generate incorrect, unsafe, or biased outputs.
• Requires careful deployment controls and output validation.
• Hardware/software compatibility depends on the Pearl miner stack and supported GPU architectures.