Fix readme's function call examples

README.md

@@ -172,14 +172,31 @@ print(tokenizer.batch_decode(model.generate(**inputs,
 ```python
 from formatron import schemas
 from formatron.formatter import FormatterBuilder
+from transformers import AutoModelForCausalLM
+import transformers
 from formatron.grammar_generators.json_generator import JsonGenerator
+from formatron.integrations.transformers import create_formatter_logits_processor_list
+import torch
 @schemas.pydantic.callable_schema
 def add(a: int, b: int, /, *, c: int):
     return a + b + c
 
+model = AutoModelForCausalLM.from_pretrained("NurtureAI/Meta-Llama-3-8B-Instruct-32k",
+                                             device_map="cuda",
+                                             torch_dtype=torch.float16)
+tokenizer = transformers.AutoTokenizer.from_pretrained("NurtureAI/Meta-Llama-3-8B-Instruct-32k")
+inputs = tokenizer(["""<|system|>
+You are a helpful assistant.<|end|>
+<|user|>a is 1, b is 6 and c is 7. Generate a json containing them.<|end|>
+<|assistant|>"""], return_tensors="pt").to("cuda")
 f = FormatterBuilder()
 f.append_line(f"{f.schema(add, JsonGenerator(), capture_name='json')}")
-# TODO: find some models that work
+logits_processor = create_formatter_logits_processor_list(tokenizer, f)
+print(tokenizer.batch_decode(model.generate(**inputs, top_p=0.5, temperature=1,
+                                            max_new_tokens=100, logits_processor=logits_processor)))
+print(logits_processor[0].formatters_captures)
+# possible output:
+# [{'json': 14}]
 ```
 ### Json Schema
 You can use [pydantic's code generator](https://docs.pydantic.dev/latest/integrations/datamodel_code_generator/)
@@ -210,6 +227,12 @@ it makes maintaining and updating the pipeline painless in the long run.
 ### Support OpenAI or in general API-based LLM solutions
 They don't support efficient logits masking per token, nullifying most benefits
 of constrained decoding.
+### Semantic Validation
+Although constrained decoding can enforce certain formats
+in generated text, they cannot guarantee that the output aligns
+with the users' intention. In other words, if the model is inadequate
+or the prompt is poorly written, it's possible to generate well-formatted
+but meaningless output.
 ### Context-Sensitive Validation
 Unfortunately, many formats require context-sensitive validation.
 For example, two keys in a JSON object must not be equal to each other.

tests/test_transformers_integration.py

@@ -91,6 +91,30 @@ class Goods(ClassSchema):
     # possible output:
     # [{'json': Goods(name='apples', price=14.4, remaining=14)}]
 
+def test_readme_example4(snapshot):
+    from formatron import schemas
+    from formatron.formatter import FormatterBuilder
+    from formatron.grammar_generators.json_generator import JsonGenerator
+    @schemas.pydantic.callable_schema
+    def add(a: int, b: int, /, *, c: int):
+        return a + b + c
+
+    model = AutoModelForCausalLM.from_pretrained("NurtureAI/Meta-Llama-3-8B-Instruct-32k",
+                                                 device_map="cuda",
+                                                 torch_dtype=torch.float16)
+    tokenizer = transformers.AutoTokenizer.from_pretrained("NurtureAI/Meta-Llama-3-8B-Instruct-32k")
+    inputs = tokenizer(["""<|system|>
+    You are a helpful assistant.<|end|>
+    <|user|>a is 1, b is 6 and c is 7. Generate a json containing them.<|end|>
+    <|assistant|>"""], return_tensors="pt").to("cuda")
+    f = FormatterBuilder()
+    f.append_line(f"{f.schema(add, JsonGenerator(), capture_name='json')}")
+    logits_processor = create_formatter_logits_processor_list(tokenizer, f)
+    print(tokenizer.batch_decode(model.generate(**inputs, top_p=0.5, temperature=1,
+                                                max_new_tokens=100, logits_processor=logits_processor)))
+    print(logits_processor[0].formatters_captures)
+    # possible output:
+    # [{'json': 14}]
 
 def test_transformers_batched_inference(snapshot):
     f = FormatterBuilder()