Different models may support different variants of these, with slightly different parameters. In order to make it easy to get LLMs to return structured output, we have added a common interface to LangChain models:.with_structured_output. By invoking this method (and passing in a JSON schema o...
Using structured output with vision models like gpt-4o-mini works. I'd like to do the same for Llama-3.2-11B-Vision-Instruct from GitHub models. Currently it throws an exception. Reproduction Steps Clone this repo:https://github.com/lqdev/AIBookmarks/ SetmodelNamein Program.cs toLlama-3.2...
It's 5 inches wide.\n</description>" }] response = client.chat.completions.create( model="databricks-meta-llama-3-1-70b-instruct", messages=messages, response_format=response_format ) print(json.dumps(response.choices[0].message.model_dump()['content'], indent=2)) ...
Gemma-2 2B fine-tuned for Structured Data Extraction This project is a collection of notebook and a simple flask web server to serve Gemma-2 using llama-cpp. The goal of this project is to fine-tune a model to get a better result on the task of to the task of extracting data into ...
partial completion exceed the token limit and cut off early before the full completion could be output by the model. This limitation may be mitigated by increasing the token limit up to 2048 (GPT-3) or 4096 (Llama-2); we expect the token limitation will become less of a concern as the...
{"role":"user","content":prompt},]response=client.chat.completions.create(model="meta/llama3-8b-instruct",messages=messages,extra_body={"nvext":{"guided_choice":choices}},stream=False)assistant_message=response.choices[0].message.contentprint(assistant_message)# Prints:# Good ...
You can specify a list of choices for the output using the guided_choice parameter in the nvext extension to the OpenAI schema. client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="not-used") choices = ["Good", "Bad", "Neutral"] prompt = (f"Return the sentiment based ...
that won’t result in the final HTML like <img src="/files/styles/thumbnail/llama.jpg" width="100" height="100" alt="Awesome llama!" />, but instead in a placeholder that the filter system will transform into the final HTML upon output: <img data-file-uuid="aa657593-0da9-42c0-...
that won’t result in the final HTML like <img src="/files/styles/thumbnail/llama.jpg" width="100" height="100" alt="Awesome llama!" />, but instead in a placeholder that the filter system will transform into the final HTML upon output: <img data-file-uuid="aa657593-0da9-42c0-...
Therefore, we utilized the llama.cpp version44, a framework originally designed to run Llama 2 models on lower-resource hardware as well as support grammar-based output formatting. Thus, we enforced the JSON format generation using llama.cpp’s grammar-based sampling, which dictates text ...