Instructions to use Tranium/helixql-nl2hql-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tranium/helixql-nl2hql-mini with Transformers:

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

pipe = pipeline("text-generation", model="Tranium/helixql-nl2hql-mini")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tranium/helixql-nl2hql-mini", dtype="auto")

PEFT
How to use Tranium/helixql-nl2hql-mini with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tranium/helixql-nl2hql-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tranium/helixql-nl2hql-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tranium/helixql-nl2hql-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tranium/helixql-nl2hql-mini

SGLang

How to use Tranium/helixql-nl2hql-mini 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 "Tranium/helixql-nl2hql-mini" \
    --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": "Tranium/helixql-nl2hql-mini",
		"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 "Tranium/helixql-nl2hql-mini" \
        --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": "Tranium/helixql-nl2hql-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tranium/helixql-nl2hql-mini with Docker Model Runner:
```
docker model run hf.co/Tranium/helixql-nl2hql-mini
```

helixql-nl2hql-mini

This repository contains a PEFT LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct.

Model Details

Parameter	Value
Base model	`Qwen/Qwen2.5-0.5B-Instruct`
Adapter repo	`Tranium/helixql-nl2hql-mini`
Training type	`qlora`
Strategy chain	SFT (3ep)
Dataset source	`local`
Datasets	`train.jsonl`, `eval.jsonl`
Batch Size	8
LoRA r	`16`
LoRA alpha	`32`
LoRA dropout	`0.05`
LoRA bias	`none`
Target modules	`all-linear`
DoRA	`False`
rsLoRA	`False`
Init	`gaussian`

Training Details

Hyperparameter	Value
epochs	`3`
learning_rate	`0.0002`
warmup_ratio	`0.05`
per_device_train_batch_size	`8`
gradient_accumulation_steps	`4`
effective_batch_size	`32`
optimizer	`paged_adamw_8bit`
lr_scheduler	`cosine`

Training Results

Run timeline

Started at: 2026-05-30T18:45:06.508188
Completed at: 2026-05-30T18:45:27.286189

Phase	Strategy	Status	train_loss	eval_loss	global_step	epoch	runtime_s	peak_mem_gb
0	sft	completed	2.1130	—	12	3.00	16.3	3.75

Usage

Load as a PEFT adapter (recommended)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "Tranium/helixql-nl2hql-mini"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto", torch_dtype="auto", trust_remote_code=False)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

prompt = "Hello!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Merge adapter into base model (optional)

merged = model.merge_and_unload()
merged.save_pretrained("merged-model")
tokenizer.save_pretrained("merged-model")

Training Infrastructure

Platform: runpod
GPU: NVIDIA GeForce RTX 3090

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Model tree for Tranium/helixql-nl2hql-mini

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

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