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PipableAI
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pip-sql-1.3b

Text Generation
Transformers
PyTorch
Safetensors
JAX
English
llama
sql
code
text2sql
instruction_tuned
basemodel
text-generation-inference
conversational
Inference Endpoints
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QagentS commited on
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Update README.md

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@@ -91,8 +91,8 @@ prompt = f"""<schema>{schema}</schema>
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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- model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
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- tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
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  inputs = tokenizer(text, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=200)
@@ -103,10 +103,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].s
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  ```python
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  from transformers import FlaxAutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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- model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
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- tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
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- inputs = tokenizer(text, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=200)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
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  ```
@@ -115,10 +115,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].s
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  ```python
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  from transformers import TFAutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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- model = TFAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
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- tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
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- inputs = tokenizer(text, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=200)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
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  ```
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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+ model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
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+ tokenizer = AutoTokenizer.from_pretrained("PPipableAI/pip-sql-1.3b")
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  inputs = tokenizer(text, return_tensors="pt")
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  outputs = model.generate(**inputs, max_new_tokens=200)
 
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  ```python
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  from transformers import FlaxAutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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+ model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
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+ tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")
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+ inputs = tokenizer(text, return_tensors="jax")
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  outputs = model.generate(**inputs, max_new_tokens=200)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
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  ```
 
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  ```python
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  from transformers import TFAutoModelForCausalLM, AutoTokenizer
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  device = "cuda"
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+ model = TFAutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
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+ tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")
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+ inputs = tokenizer(text, return_tensors="tf")
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  outputs = model.generate(**inputs, max_new_tokens=200)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
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  ```