Instructions to use sharp-pixel/opensearch-agentic-dsl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sharp-pixel/opensearch-agentic-dsl with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-3-8B-Instruct-2512-BF16")
model = PeftModel.from_pretrained(base_model, "sharp-pixel/opensearch-agentic-dsl")

Transformers

How to use sharp-pixel/opensearch-agentic-dsl with Transformers:

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

pipe = pipeline("text-generation", model="sharp-pixel/opensearch-agentic-dsl")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sharp-pixel/opensearch-agentic-dsl", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sharp-pixel/opensearch-agentic-dsl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sharp-pixel/opensearch-agentic-dsl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sharp-pixel/opensearch-agentic-dsl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sharp-pixel/opensearch-agentic-dsl

SGLang

How to use sharp-pixel/opensearch-agentic-dsl 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 "sharp-pixel/opensearch-agentic-dsl" \
    --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": "sharp-pixel/opensearch-agentic-dsl",
		"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 "sharp-pixel/opensearch-agentic-dsl" \
        --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": "sharp-pixel/opensearch-agentic-dsl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sharp-pixel/opensearch-agentic-dsl with Docker Model Runner:
```
docker model run hf.co/sharp-pixel/opensearch-agentic-dsl
```

opensearch-agentic-dsl

A LoRA adapter that translates English shopper requests into OpenSearch DSL for a second-hand luxury catalog. It is designed for OpenSearch's native Agentic Search QueryPlanningTool.

The adapter uses mistralai/Ministral-3-8B-Instruct-2512-BF16 at revision 06cc81bfd6e45321d8fc8f816576c5b6ac67ec22. Load both the base model and tokenizer at this revision, then attach the adapter with PEFT.

Input and output

Input combines shopper text, trusted application controls, the index mapping, and allowed query fields. The exact system prompt and user template are included. The complete example shows how to construct the three-line question and double JSON encode the mapping and field list.

Output is one JSON request body containing size, integer track_total_hits, query, and optional sort. The planner applies required filters, price ceilings, and the selected ranking mode. Persona expansion contributes optional scoring. Index selection, search pipelines, and _source stay application-owned.

Run the model

Download sharp-pixel/opensearch-agentic-dsl and run the included example:

hf download sharp-pixel/opensearch-agentic-dsl --local-dir opensearch-agentic-dsl
cd opensearch-agentic-dsl
python example.py

The example requires a CUDA GPU with enough memory for the BF16 base model and uses Transformers 5.13.0, PEFT 0.19.1, and PyTorch 2.13.0. It loads the bundled prompts and local adapter, uses greedy decoding, and prints the generated body. Set ADAPTER_ID=sharp-pixel/opensearch-agentic-dsl to load the adapter from the Hub instead.

For vLLM, enable LoRA with maximum rank 16, load the language model only, and serve the adapter as opensearch-agentic-dsl.

Training and evaluation

Training used 4,640 synthetic examples and 1,160 held-out examples, grouped to keep related scenarios within a single split. Only the language-model attention and MLP projections were adapted; the vision encoder and projector stayed frozen.

Setting	Value
Method	QLoRA with completion-only supervision
Quantization / compute	4-bit NF4 / BF16
LoRA rank / alpha / dropout	16 / 32 / 0.05
Epochs / sequence length	2 / 2,112 tokens
Learning rate / effective batch size	0.0001 / 16
GPU / training time	RTX PRO 6000 Blackwell / 1.76 hours

Metric	Value
Training loss	0.0038393672
Held-out loss	0.0016606329
Held-out token accuracy	99.9305%

Token accuracy is measured with teacher-forced targets, not freely generated requests. The synthetic evaluation does not establish real-world retrieval quality. Validate generated DSL against the actual mapping and application policy, and enforce OpenSearch permissions and resource limits.

Training data, prompts, policy, and the dependency lockfile match this verified source revision.

License

The adapter and accompanying model-card documentation are released under Apache 2.0. The upstream model and tokenizer are also Apache 2.0; see NOTICE for attribution. Application source and training data retain their respective licenses.

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Model tree for sharp-pixel/opensearch-agentic-dsl

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mistralai/Ministral-3-8B-Base-2512

Finetuned

mistralai/Ministral-3-8B-Instruct-2512-BF16

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