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fix: further simplify examples

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149
docs/source/basic_tutorials/using_guidance.md
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@ -74,6 +74,45 @@ curl localhost:3000/generate \
```
### Hugging Face Hub Python Library
The Hugging Face Hub Python library provides a client that makes it easy to interact with the Messages API. Here's an example of how to use the client to send a request with a grammar parameter.
```python
from huggingface_hub import InferenceClient
client = InferenceClient("http://localhost:3000")
schema = {
"properties": {
"location": {"title": "Location", "type": "string"},
"activity": {"title": "Activity", "type": "string"},
"animals_seen": {
"maximum": 5,
"minimum": 1,
"title": "Animals Seen",
"type": "integer",
},
"animals": {"items": {"type": "string"}, "title": "Animals", "type": "array"},
},
"required": ["location", "activity", "animals_seen", "animals"],
"title": "Animals",
"type": "object",
}
user_input = "I saw a puppy a cat and a raccoon during my bike ride in the park"
resp = client.text_generation(
f"convert to JSON: 'f{user_input}'. please use the following schema: {schema}",
max_new_tokens=100,
seed=42,
grammar={"type": "json", "value": schema},
)
print(resp)
# { "activity": "bike ride", "animals": ["puppy", "cat", "raccoon"], "animals_seen": 3, "location": "park" }
```
A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The LLM will then generate a response that conforms to the specified grammar.
> Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster.
@ -83,125 +122,55 @@ A grammar can be defined using Pydantic models, JSON schemas, or regular express
Using Pydantic models we can define a similar grammar as the previous example in a shorter and more readable way.
```python
import requests
from huggingface_hub import InferenceClient
from pydantic import BaseModel, conint
from typing import List
class Animals(BaseModel):
location: str
activity: str
animals_seen: conint(ge=1, le=5) # Constrained integer type
animals: List[str]
prompt = "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park"
data = {
"inputs": prompt,
"parameters": {
"repetition_penalty": 1.3,
"grammar": {
"type": "json",
"value": Animals.schema()
}
}
}
client = InferenceClient("http://localhost:3000")
headers = {
"Content-Type": "application/json",
}
response = requests.post(
'http://127.0.0.1:3000/generate',
headers=headers,
json=data
user_input = "I saw a puppy a cat and a raccoon during my bike ride in the park"
resp = client.text_generation(
f"convert to JSON: 'f{user_input}'. please use the following schema: {Animals.schema()}",
max_new_tokens=100,
seed=42,
grammar={"type": "json", "value": Animals.schema()},
)
print(response.json())
# {'generated_text': '{ "activity": "bike riding", "animals": ["puppy","cat","raccoon"],"animals_seen": 3, "location":"park" }'}
print(resp)
# { "activity": "bike ride", "animals": ["puppy", "cat", "raccoon"], "animals_seen": 3, "location": "park" }
```
### JSON Schema Integration
If Pydantic's not your style, go raw with direct JSON Schema integration. This is similar to the first example but with programmatic control.
```python
import requests
json_schema = {
"properties": {
"location": {
"type": "string"
},
"activity": {
"type": "string"
},
"animals_seen": {
"type": "integer",
"minimum": 1,
"maximum": 5
},
"animals": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": ["location", "activity", "animals_seen", "animals"]
}
data = {
"inputs": "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park",
"parameters": {
"max_new_tokens": 200,
"repetition_penalty": 1.3,
"grammar": {
"type": "json",
"value": json_schema
}
}
}
headers = {
"Content-Type": "application/json",
}
response = requests.post(
'http://127.0.0.1:3000/generate',
headers=headers,
json=data
)
print(response.json())
# {'generated_text': '{\n"activity": "biking",\n"animals": ["puppy","cat","raccoon"]\n , "animals_seen": 3,\n "location":"park"}'}
```
### Hugging Face Hub Python Library
The Hugging Face Hub Python library provides a client that makes it easy to interact with the Messages API. Here's an example of how to use the client to send a request with a grammar parameter.
defining a grammar as regular expressions
```python
from huggingface_hub import InferenceClient
client = InferenceClient(
"http://localhost:3000" # local endpoint
# "meta-llama/Meta-Llama-3-8B-Instruct" # HF serverless endpoint
)
client = InferenceClient("http://localhost:3000")
regexp = "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)"
resp = client.text_generation(
"Whats Googles DNS",
max_new_tokens=10,
decoder_input_details=True,
f"Whats Googles DNS? Please use the following regex: {regexp}",
seed=42,
grammar={
"type": "regex",
"value": "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)",
"value": regexp,
},
)
print(resp)
# 1.0.0.1
# 7.1.1.1
```

2
docs/source/basic_tutorials/visual_language_models.md
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@ -115,8 +115,6 @@ print(chat_completion)
# ChatCompletion(id='', choices=[Choice(finish_reason='eos_token', index=0, logprobs=None, message=ChatCompletionMessage(content=' The image depicts an anthropomorphic rabbit dressed in a space suit with gear that resembles NASA attire. The setting appears to be a solar eclipse with dramatic mountain peaks and a partial celestial body in the sky. The artwork is detailed and vivid, with a warm color palette and a sense of an adventurous bunny exploring or preparing for a journey beyond Earth. ', role='assistant', function_call=None, tool_calls=None))], created=1714589732, model='llava-hf/llava-v1.6-mistral-7b-hf', object='text_completion', system_fingerprint='2.0.2-native', usage=CompletionUsage(completion_tokens=84, prompt_tokens=2943, total_tokens=3027))
```
If you want additional details, you can add `details=True`. In this case, you get a `TextGenerationStreamResponse` which contains additional information such as the probabilities and the tokens. For the final response in the stream, it also returns the full generated text.
### Inference Through Sending `cURL` Requests
To use the `generate_stream` endpoint with curl, you can add the `-N` flag. This flag disables curl default buffering and shows data as it arrives from the server.