Instructions to use sriksven/SQLForge-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sriksven/SQLForge-7B with Transformers:

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

pipe = pipeline("text-generation", model="sriksven/SQLForge-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sriksven/SQLForge-7B")
model = AutoModelForCausalLM.from_pretrained("sriksven/SQLForge-7B")
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 sriksven/SQLForge-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sriksven/SQLForge-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sriksven/SQLForge-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sriksven/SQLForge-7B

SGLang

How to use sriksven/SQLForge-7B 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 "sriksven/SQLForge-7B" \
    --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": "sriksven/SQLForge-7B",
		"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 "sriksven/SQLForge-7B" \
        --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": "sriksven/SQLForge-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use sriksven/SQLForge-7B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sriksven/SQLForge-7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sriksven/SQLForge-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sriksven/SQLForge-7B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="sriksven/SQLForge-7B",
    max_seq_length=2048,
)

Docker Model Runner
How to use sriksven/SQLForge-7B with Docker Model Runner:
```
docker model run hf.co/sriksven/SQLForge-7B
```

SQLForge-7B / README.md

sriksven

Create README.md

bf7c7f8 verified about 6 hours ago

preview code

raw

history blame contribute delete

4.35 kB

	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- sql
	- text-to-sql
	- qlora
	- unsloth
	- qwen2.5
	- database
	- natural-language-to-sql
	datasets:
	- gretelai/synthetic_text_to_sql
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	model-index:
	- name: SQLForge-7B
	results: []
	---

	# SQLForge-7B

	A fine-tuned Qwen2.5-7B-Instruct model specialized for natural language to SQL generation. Given a database schema and a question in plain English, it writes the correct SQL query and explains what it does.

	## Key Details

	\| \| \|
	\|---\|---\|
	\| Base model \| Qwen/Qwen2.5-7B-Instruct \|
	\| Method \| QLoRA (4-bit NF4, rank 16, alpha 16) \|
	\| Library \| Unsloth + TRL SFTTrainer \|
	\| Dataset \| gretelai/synthetic_text_to_sql (10K examples from 100K) \|
	\| Hardware \| NVIDIA RTX A5000 (24GB VRAM) on RunPod \|
	\| Training time \| ~2.75 hours (500 steps) \|
	\| Final loss \| 0.414 \|
	\| Parameters trained \| 40.4M of 7.66B (0.53%) \|
	\| Format \| ChatML \|
	\| Output \| Merged 16-bit safetensors \|

	## Dataset

	Trained on 10,000 examples from the [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) dataset, which covers 100 domains with a wide range of SQL complexity levels including subqueries, joins, aggregations, window functions, and set operations. Each example includes the database schema (CREATE TABLE statements), a natural language question, the correct SQL query, and an explanation.

	## Usage

	### Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("sriksven/SQLForge-7B")
	tokenizer = AutoTokenizer.from_pretrained("sriksven/SQLForge-7B")

	messages = [
	{
	"role": "system",
	"content": "You are an expert SQL assistant. Given a database schema and a natural language question, write the correct SQL query and explain what it does.",
	},
	{
	"role": "user",
	"content": (
	"Schema:\n"
	"CREATE TABLE employees (id INT, name VARCHAR(100), department VARCHAR(50), salary DECIMAL(10,2));\n"
	"CREATE TABLE departments (name VARCHAR(50), budget DECIMAL(12,2));\n\n"
	"Question: What is the average salary by department, only showing departments with average salary above 75000?"
	),
	},
	]

	inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
	outputs = model.generate(inputs, max_new_tokens=512)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	### Unsloth (faster inference)

	```python
	from unsloth import FastLanguageModel

	model, tokenizer = FastLanguageModel.from_pretrained(
	model_name="sriksven/SQLForge-7B",
	max_seq_length=2048,
	load_in_4bit=True,
	)
	FastLanguageModel.for_inference(model)
	```

	## SQL Complexity Coverage

	The training data includes queries across multiple complexity levels:
	- Simple SELECT with WHERE clauses
	- Aggregations with GROUP BY and HAVING
	- Single and multiple JOINs
	- Subqueries and correlated subqueries
	- Window functions (ROW_NUMBER, RANK, LAG, LEAD)
	- Set operations (UNION, INTERSECT, EXCEPT)
	- Data definition (CREATE, ALTER, INSERT)

	## Intended Use

	- Natural language interfaces to databases
	- SQL copilot tools for analysts and developers
	- Educational tools for learning SQL
	- Prototyping data query systems

	## Limitations

	- Trained on synthetic data, not real production database queries
	- May not handle highly domain-specific or proprietary SQL dialects
	- Best with standard SQL syntax (PostgreSQL/MySQL style)
	- Does not validate against a live database — SQL correctness is not guaranteed
	- Long or deeply nested schemas may exceed the 2048 token context

	## Training Infrastructure

	\| \| \|
	\|---\|---\|
	\| GPU \| NVIDIA RTX A5000 24GB \|
	\| Cloud \| RunPod ($0.27/hr) \|
	\| Framework \| Unsloth 2026.5.2 + TRL + Transformers 5.5.0 \|
	\| Precision \| BF16 training, 4-bit NF4 base quantization \|
	\| Optimizer \| AdamW 8-bit \|
	\| Learning rate \| 2e-4, linear decay \|
	\| Batch size \| 16 effective (4 per device × 4 accumulation) \|
	\| Packing \| Enabled \|

	## Source Code

	Training scripts: [github.com/sriksven/LLM-FineTune-Suite](https://github.com/sriksven/LLM-FineTune-Suite)

	## License

	Apache 2.0