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zenlm
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zen-sql

Text Generation
Transformers
Safetensors
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qwen3
zen
zenlm
hanzo-ai
sql
database
code-generation
text-generation-inference
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fix(readme): body:sub('huggingface\\.co/zenlm/zen4(?![-\\w])')x1;sub('"zenlm/zen4"')x2;sub('zenlm/zen4(?![-\\w])')x1

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  1. README.md +3 -3
README.md CHANGED
@@ -22,12 +22,12 @@ SQL specialist for complex query generation, schema design, query optimization,
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  Supports PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more.
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- Base weights: [zenlm/zen4](https://huggingface.co/zenlm/zen4)
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto")
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- tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4")
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  messages = [{"role": "user", "content": "Your domain-specific prompt here"}]
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  text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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  inputs = tokenizer(text, return_tensors="pt").to(model.device)
 
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  Supports PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more.
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+ Base weights: [zenlm/zen-pro](https://huggingface.co/zenlm/zen-pro)
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model = AutoModelForCausalLM.from_pretrained("zenlm/zen-pro", torch_dtype="auto")
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+ tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-pro")
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  messages = [{"role": "user", "content": "Your domain-specific prompt here"}]
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  text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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  inputs = tokenizer(text, return_tensors="pt").to(model.device)