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README.md

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+
+---
+library_name: gguf
+license: apache-2.0
+base_model:
+- Ellbendls/Qwen-3-4b-Text_to_SQL
+- Qwen/Qwen3-4B-Instruct-2507
+tags:
+- gguf
+- llama.cpp
+- qwen
+- text-to-sql
+- sql
+- instruct
+language:
+- eng
+- zho
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+---
+
 # Ellbendls/Qwen-3-4b-Text_to_SQL-GGUF
 
-Derived GGUF exports of `Ellbendls/Qwen-3-4b-Text_to_SQL`
+Quantized GGUF builds of `Ellbendls/Qwen-3-4b-Text_to_SQL` for fast CPU/GPU inference with llama.cpp-compatible runtimes.
+
+- **Base model**. Fine-tuned from **Qwen/Qwen3-4B-Instruct-2507** for Text-to-SQL.
+- **License**. Apache-2.0 (inherits from base). Keep attribution.
+- **Purpose**. Turn natural language into SQL. When schema is missing, the model can infer a simple schema then produce SQL.
+
+## Files
+
+Base and quantized variants:
+
+- `Qwen-3-4b-Text_to_SQL-F16.gguf`  — reference float16 export
+- `Qwen-3-4b-Text_to_SQL-q2_k.gguf`
+- `Qwen-3-4b-Text_to_SQL-q3_k_m.gguf`
+- `Qwen-3-4b-Text_to_SQL-q4_k_m.gguf`  ← good default
+- `Qwen-3-4b-Text_to_SQL-q5_k_m.gguf`
+- `Qwen-3-4b-Text_to_SQL-q8_0.gguf`    ← near-lossless, larger
+
+Conversion and quantization done with `llama.cpp`.
+
+## Recommended pick
+
+- **Q4_K_M**. Best balance of speed and quality for laptops and small servers.
+- **Q5_K_M**. Higher quality, a bit more RAM/VRAM.
+- **Q8_0**. Highest quality among quants. Use if you have headroom.
+
+## Approximate memory needs
+
+These are ballpark for a 4B model. Real usage varies by runtime and context length.
+
+- Q4_K_M: 3–4 GB RAM/VRAM
+- Q5_K_M: 4–5 GB
+- Q8_0: 6–8 GB
+- F16: 10–12 GB
+
+## Quick start
+
+### llama.cpp (CLI)
+
+CPU only:
+```bash
+./llama-cli -m Qwen-3-4b-Text_to_SQL-q4_k_m.gguf \
+  -p "Generate SQL to get average salary by department in 2024." \
+  -n 256 -t 6
+````
+
+NVIDIA GPU offload (build with `-DLLAMA_CUBLAS=ON`):
+
+```bash
+./llama-cli -m Qwen-3-4b-Text_to_SQL-q4_k_m.gguf \
+  -p "Generate SQL to get average salary by department in 2024." \
+  -n 256 -ngl 999 -t 6
+```
+
+### Python (llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="Qwen-3-4b-Text_to_SQL-q4_k_m.gguf", n_ctx=4096, n_gpu_layers=35)  # set 0 for CPU-only
+prompt = "Generate SQL to list total orders and revenue by month for 2024."
+out = llm(prompt, max_tokens=256, temperature=0.2, top_p=0.9)
+print(out["choices"][0]["text"].strip())
+```
+
+### LM Studio / Kobold / text-generation-webui
+
+* Select the `.gguf` file and load.
+* Set temperature 0.1–0.3 for deterministic SQL.
+* Use a system prompt to anchor behavior.
+
+## Prompting tips (Text-to-SQL)
+
+Use clear instructions. Give schema if you have it. Ask for SQL only.
+
+**With schema**
+
+```
+You are a Text-to-SQL generator. Return only SQL.
+Schema:
+tables: employees(emp_id, name, dept_id, salary, hired_at)
+departments(dept_id, name)
+
+Task:
+Average salary by department for year 2024. Use ANSI SQL.
+```
+
+**Without schema**
+
+```
+You are a Text-to-SQL generator. Return only SQL.
+If schema missing, assume a minimal reasonable schema.
+
+Task:
+Top 5 products by revenue in Q2 2024. Use ANSI SQL.
+```
+
+## Model details
+
+* **Base**. `Qwen/Qwen3-4B-Instruct-2507` (32k context, multilingual).
+* **Fine-tune**. Trained on `gretelai/synthetic_text_to_sql`.
+* **Task**. NL → SQL. Capable of simple schema inference when needed.
+* **Languages**. Works best in English. Can follow prompts in several languages from the base model.
+
+## Conversion reproducibility
+
+Export used:
+
+```bash
+python convert_hf_to_gguf.py /path/to/hf_model --outtype f16 --outfile Qwen-3-4b-Text_to_SQL-F16.gguf
+```
+
+Quantization used:
+
+```bash
+./llama-quantize Qwen-3-4b-Text_to_SQL-F16.gguf Qwen-3-4b-Text_to_SQL-q4_k_m.gguf Q4_K_M
+# likewise for q2_k, q3_k_m, q5_k_m, q8_0
+```
+
+## Intended use and limits
+
+* **Use**. Analytics, reporting, dashboards, data exploration, SQL prototyping.
+* **Limits**. No database connectivity. It only generates SQL text. Validate and test queries before use in production. Provide real schema for best accuracy.
+
+## Attribution
+
+* Base model: [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+* Fine-tuned model: [`Ellbendls/Qwen-3-4b-Text_to_SQL`](https://huggingface.co/Ellbendls/Qwen-3-4b-Text_to_SQL)
+
+## License
+
+Apache-2.0. Include license and NOTICE from upstream when redistributing the weights. Do not imply endorsement from Qwen or original authors.
+
+## Changelog
 
-Files:
-- Base: `Qwen-3-4b-Text_to_SQL-F16.gguf`
-- Quants: Qwen-3-4b-Text_to_SQL-q2_k.gguf, Qwen-3-4b-Text_to_SQL-q3_k_m.gguf, Qwen-3-4b-Text_to_SQL-q4_k_m.gguf, Qwen-3-4b-Text_to_SQL-q5_k_m.gguf, Qwen-3-4b-Text_to_SQL-q8_0.gguf
+* 2025-09-17. Initial GGUF release. Added q2\_k, q3\_k\_m, q4\_k\_m, q5\_k\_m, q8\_0, and F16.
 
-Converted and quantized with llama.cpp.
-Attribution: base model `Ellbendls/Qwen-3-4b-Text_to_SQL` (see original license).
-Generated: 2025-09-17T01:33:07.726161Z
+```
+::contentReference[oaicite:0]{index=0}
+```