feat: update client to 0.7 (#1667)

Close #1652

clients/python/README.md

@@ -107,7 +107,19 @@ print(text)
 ### Types
 
 ```python
-# Request Parameters
+# enum for grammar type
+class GrammarType(Enum):
+    Json = "json"
+    Regex = "regex"
+
+
+# Grammar type and value
+class Grammar:
+    # Grammar type
+    type: GrammarType
+    # Grammar value
+    value: Union[str, dict]
+
 class Parameters:
     # Activate logits sampling
     do_sample: bool
@@ -116,6 +128,10 @@ class Parameters:
     # The parameter for repetition penalty. 1.0 means no penalty.
     # See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
     repetition_penalty: Optional[float]
+    # The parameter for frequency penalty. 1.0 means no penalty
+    # Penalize new tokens based on their existing frequency in the text so far,
+    # decreasing the model's likelihood to repeat the same line verbatim.
+    frequency_penalty: Optional[float]
     # Whether to prepend the prompt to the generated text
     return_full_text: bool
     # Stop generating tokens if a member of `stop_sequences` is generated
@@ -138,10 +154,22 @@ class Parameters:
     best_of: Optional[int]
     # Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
     watermark: bool
+    # Get generation details
+    details: bool
     # Get decoder input token logprobs and ids
     decoder_input_details: bool
     # Return the N most likely tokens at each step
     top_n_tokens: Optional[int]
+    # grammar to use for generation
+    grammar: Optional[Grammar]
+
+class Request:
+    # Prompt
+    inputs: str
+    # Generation parameters
+    parameters: Optional[Parameters]
+    # Whether to stream output tokens
+    stream: bool
 
 # Decoder input tokens
 class InputToken:
@@ -161,7 +189,7 @@ class Token:
     # Token text
     text: str
     # Logprob
-    logprob: float
+    logprob: Optional[float]
     # Is the token a special token
     # Can be used to ignore tokens when concatenating
     special: bool

clients/python/pyproject.toml

 [tool.poetry]
 name = "text-generation"
-version = "0.6.1"
+version = "0.7.0"
 description = "Hugging Face Text Generation Python Client"
 license = "Apache-2.0"
 authors = ["Olivier Dehaene <olivier@huggingface.co>"]

clients/python/text_generation/client.py

@@ -67,6 +67,7 @@ class Client:
     def chat(
         self,
         messages: List[Message],
+        repetition_penalty: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         logit_bias: Optional[List[float]] = None,
         logprobs: Optional[bool] = None,
@@ -87,9 +88,13 @@ class Client:
         Args:
             messages (`List[Message]`):
                 List of messages
-            frequency_penalty (`float`):
-                The parameter for frequency penalty. 0.0 means no penalty. See [this
+            repetition_penalty (`float`):
+                The parameter for repetition penalty. 0.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 0.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             logit_bias (`List[float]`):
                 Adjust the likelihood of specified tokens
             logprobs (`bool`):
@@ -121,6 +126,7 @@ class Client:
         request = ChatRequest(
             model="tgi",
             messages=messages,
+            repetition_penalty=repetition_penalty,
             frequency_penalty=frequency_penalty,
             logit_bias=logit_bias,
             logprobs=logprobs,
@@ -179,6 +185,7 @@ class Client:
         max_new_tokens: int = 20,
         best_of: Optional[int] = None,
         repetition_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
         return_full_text: bool = False,
         seed: Optional[int] = None,
         stop_sequences: Optional[List[str]] = None,
@@ -207,6 +214,10 @@ class Client:
             repetition_penalty (`float`):
                 The parameter for repetition penalty. 1.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 1.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             return_full_text (`bool`):
                 Whether to prepend the prompt to the generated text
             seed (`int`):
@@ -245,6 +256,7 @@ class Client:
             do_sample=do_sample,
             max_new_tokens=max_new_tokens,
             repetition_penalty=repetition_penalty,
+            frequency_penalty=frequency_penalty,
             return_full_text=return_full_text,
             seed=seed,
             stop=stop_sequences if stop_sequences is not None else [],
@@ -278,6 +290,7 @@ class Client:
         do_sample: bool = False,
         max_new_tokens: int = 20,
         repetition_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
         return_full_text: bool = False,
         seed: Optional[int] = None,
         stop_sequences: Optional[List[str]] = None,
@@ -303,6 +316,10 @@ class Client:
             repetition_penalty (`float`):
                 The parameter for repetition penalty. 1.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 1.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             return_full_text (`bool`):
                 Whether to prepend the prompt to the generated text
             seed (`int`):
@@ -340,6 +357,7 @@ class Client:
             do_sample=do_sample,
             max_new_tokens=max_new_tokens,
             repetition_penalty=repetition_penalty,
+            frequency_penalty=frequency_penalty,
             return_full_text=return_full_text,
             seed=seed,
             stop=stop_sequences if stop_sequences is not None else [],
@@ -435,6 +453,7 @@ class AsyncClient:
     async def chat(
         self,
         messages: List[Message],
+        repetition_penalty: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         logit_bias: Optional[List[float]] = None,
         logprobs: Optional[bool] = None,
@@ -455,9 +474,13 @@ class AsyncClient:
         Args:
             messages (`List[Message]`):
                 List of messages
-            frequency_penalty (`float`):
+            repetition_penalty (`float`):
                 The parameter for frequency penalty. 0.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 0.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             logit_bias (`List[float]`):
                 Adjust the likelihood of specified tokens
             logprobs (`bool`):
@@ -489,6 +512,7 @@ class AsyncClient:
         request = ChatRequest(
             model="tgi",
             messages=messages,
+            repetition_penalty=repetition_penalty,
             frequency_penalty=frequency_penalty,
             logit_bias=logit_bias,
             logprobs=logprobs,
@@ -546,6 +570,7 @@ class AsyncClient:
         max_new_tokens: int = 20,
         best_of: Optional[int] = None,
         repetition_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
         return_full_text: bool = False,
         seed: Optional[int] = None,
         stop_sequences: Optional[List[str]] = None,
@@ -574,6 +599,10 @@ class AsyncClient:
             repetition_penalty (`float`):
                 The parameter for repetition penalty. 1.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 1.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             return_full_text (`bool`):
                 Whether to prepend the prompt to the generated text
             seed (`int`):
@@ -614,6 +643,7 @@ class AsyncClient:
             do_sample=do_sample,
             max_new_tokens=max_new_tokens,
             repetition_penalty=repetition_penalty,
+            frequency_penalty=frequency_penalty,
             return_full_text=return_full_text,
             seed=seed,
             stop=stop_sequences if stop_sequences is not None else [],
@@ -644,6 +674,7 @@ class AsyncClient:
         do_sample: bool = False,
         max_new_tokens: int = 20,
         repetition_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
         return_full_text: bool = False,
         seed: Optional[int] = None,
         stop_sequences: Optional[List[str]] = None,
@@ -669,6 +700,10 @@ class AsyncClient:
             repetition_penalty (`float`):
                 The parameter for repetition penalty. 1.0 means no penalty. See [this
                 paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+            frequency_penalty (`float`):
+                The parameter for frequency penalty. 1.0 means no penalty
+                Penalize new tokens based on their existing frequency in the text so far,
+                decreasing the model's likelihood to repeat the same line verbatim.
             return_full_text (`bool`):
                 Whether to prepend the prompt to the generated text
             seed (`int`):
@@ -706,6 +741,7 @@ class AsyncClient:
             do_sample=do_sample,
             max_new_tokens=max_new_tokens,
             repetition_penalty=repetition_penalty,
+            frequency_penalty=frequency_penalty,
             return_full_text=return_full_text,
             seed=seed,
             stop=stop_sequences if stop_sequences is not None else [],

clients/python/text_generation/types.py

@@ -109,7 +109,12 @@ class ChatRequest(BaseModel):
     model: str
     # List of messages in the conversation
     messages: List[Message]
-    # Penalty for frequency of new tokens
+    # The parameter for repetition penalty. 1.0 means no penalty.
+    # See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+    repetition_penalty: Optional[float] = None
+    # The parameter for frequency penalty. 1.0 means no penalty
+    # Penalize new tokens based on their existing frequency in the text so far,
+    # decreasing the model's likelihood to repeat the same line verbatim.
     frequency_penalty: Optional[float] = None
     # Bias values for token selection
     logit_bias: Optional[List[float]] = None
@@ -145,6 +150,10 @@ class Parameters(BaseModel):
     # The parameter for repetition penalty. 1.0 means no penalty.
     # See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
     repetition_penalty: Optional[float] = None
+    # The parameter for frequency penalty. 1.0 means no penalty
+    # Penalize new tokens based on their existing frequency in the text so far,
+    # decreasing the model's likelihood to repeat the same line verbatim.
+    frequency_penalty: Optional[float] = None
     # Whether to prepend the prompt to the generated text
     return_full_text: bool = False
     # Stop generating tokens if a member of `stop_sequences` is generated
@@ -201,6 +210,12 @@ class Parameters(BaseModel):
             raise ValidationError("`repetition_penalty` must be strictly positive")
         return v
 
+    @field_validator("frequency_penalty")
+    def valid_frequency_penalty(cls, v):
+        if v is not None and v <= 0:
+            raise ValidationError("`frequency_penalty` must be strictly positive")
+        return v
+
     @field_validator("seed")
     def valid_seed(cls, v):
         if v is not None and v < 0: