Instructions to use Aishwarya0803/smolified-tiny-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aishwarya0803/smolified-tiny-text-to-sql with Transformers:

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

pipe = pipeline("text-generation", model="Aishwarya0803/smolified-tiny-text-to-sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aishwarya0803/smolified-tiny-text-to-sql")
model = AutoModelForCausalLM.from_pretrained("Aishwarya0803/smolified-tiny-text-to-sql")
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 Aishwarya0803/smolified-tiny-text-to-sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aishwarya0803/smolified-tiny-text-to-sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aishwarya0803/smolified-tiny-text-to-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aishwarya0803/smolified-tiny-text-to-sql

SGLang

How to use Aishwarya0803/smolified-tiny-text-to-sql 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 "Aishwarya0803/smolified-tiny-text-to-sql" \
    --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": "Aishwarya0803/smolified-tiny-text-to-sql",
		"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 "Aishwarya0803/smolified-tiny-text-to-sql" \
        --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": "Aishwarya0803/smolified-tiny-text-to-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aishwarya0803/smolified-tiny-text-to-sql with Docker Model Runner:
```
docker model run hf.co/Aishwarya0803/smolified-tiny-text-to-sql
```

Aishwarya0803 commited on Mar 28

Commit

9d059ae

verified ·

1 Parent(s): 27148c8

Unsloth Model Card

Browse files

Files changed (1) hide show

README.md +12 -60

README.md CHANGED Viewed

@@ -1,69 +1,21 @@
 ---
-license: apache-2.0
-language:
-- en
 tags:
 - text-generation-inference
 - transformers
-- smolify
-- dslm
-pipeline_tag: text-generation
-inference:
-  parameters:
-    temperature: 1
-    top_p: 0.95
-    top_k: 64
 ---
-# 🤏 smolified-tiny-text-to-sql
-> **Intelligence, Distilled.**
-This is a **Domain Specific Language Model (DSLM)** generated by the **Smolify Foundry**.
-It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.
-## 📦 Asset Details
-- **Origin:** Smolify Foundry (Job ID: `d529bcce`)
-- **Architecture:** gemma-3-270m
-- **Training Method:** Proprietary Neural Distillation
-- **Optimization:** 4-bit Quantized / FP16 Mixed
-- **Dataset:** [Link to Dataset](https://huggingface.co/datasets/Aishwarya0803/smolified-tiny-text-to-sql)
-## 🚀 Usage (Inference)
-This model is compatible with standard inference backends like vLLM, and Hugging Face Transformers.
-```python
-# Example: Running your Sovereign Model
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "Aishwarya0803/smolified-tiny-text-to-sql"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
-messages = [
-    {"role": "system", "content": '''You are a SQL generator. Schema: Table 'orders' (id, customer_name, amount, status, date). Translate the user question into a valid SQLite query. Output SQL only.'''},
-    {"role": "user", "content": '''Which customers have spent more than 1000 in total?'''}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize = False,
-    add_generation_prompt = True,
-)
-if "gemma-3-270m" == "gemma-3-270m":
-    text = text.removeprefix('<bos>')
-from transformers import TextStreamer
-_ = model.generate(
-    **tokenizer(text, return_tensors = "pt").to(model.device),
-    max_new_tokens = 1000,
-    temperature = 1.0, top_p = 0.95, top_k = 64,
-    streamer = TextStreamer(tokenizer, skip_prompt = True),
-)
-```
-## ⚖️ License & Ownership
-This model weights are a sovereign asset owned by **Aishwarya0803**.
-Generated via [Smolify.ai](https://smolify.ai).
-[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)

 ---
+base_model: unsloth/gemma-3-270m-it
 tags:
 - text-generation-inference
 - transformers
+- unsloth
+- gemma3_text
+license: apache-2.0
+language:
+- en
 ---
+# Uploaded finetuned  model
+- **Developed by:** Aishwarya0803
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/gemma-3-270m-it
+This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)