Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: apache-2.0Usage
Serve with vLLM (OpenAI-compatible):
bash
vllm serve Qwen/Qwen3-4B-Instruct-2507 \--enable-lora --lora-modules linkd-dsl=ericmao/linkd-dsl-qwen3-4b-lora \--max-model-len 2048 \--speculative-config '{"method":"ngram","num_speculative_tokens":8,"prompt_lookup_max":4,"prompt_lookup_min":2}'
Then call it with the exact production prompt (see the linkd-search repo,
slm/common.py:SYSTEM_PROMPT), model="linkd-dsl", temperature=0. The
response is a raw JSON Mongo filter; run it as
collection.find(filter).limit(20).
A merged full-weights variant (no LoRA runtime needed) is published at
ericmao/linkd-dsl-qwen3-4b.
Model provider
ericmao
Model tree
Base
Qwen/Qwen3-4B-Instruct-2507
Adapter
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information