| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | tags: |
| | - zen |
| | - zenlm |
| | - hanzo-ai |
| | - sql |
| | - database |
| | - code-generation |
| | pipeline_tag: text-generation |
| | library_name: transformers |
| | base_model: zenlm/zen4 |
| | --- |
| | |
| | # Zen Sql |
| |
|
| | > **Parameters**: 7B | **Architecture**: Zen 4 Architecture | **Context**: 32K | **License**: Apache 2.0 | **Released**: 2024-11-15 |
| |
|
| | SQL specialist for complex query generation, schema design, query optimization, and database documentation. |
| |
|
| | Supports PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more. |
| |
|
| | Base weights: [zenlm/zen4](https://huggingface.co/zenlm/zen4) |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto") |
| | tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4") |
| | messages = [{"role": "user", "content": "Your domain-specific prompt here"}] |
| | text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| | inputs = tokenizer(text, return_tensors="pt").to(model.device) |
| | output = model.generate(**inputs, max_new_tokens=1024) |
| | print(tokenizer.decode(output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)) |
| | ``` |
| |
|
| | --- |
| | ## The Zen LM Family |
| |
|
| | Joint research between **Hanzo AI** (Techstars '17), **Zoo Labs Foundation** (501c3), and **Lux Partners Limited**. |
| |
|
| | All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially. |
| |
|
| | [HuggingFace](https://huggingface.co/zenlm) 路 [Chat](https://hanzo.chat) 路 [API](https://api.hanzo.ai) 路 [Docs](https://zenlm.org) |
| |
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