cohereai_classify table | CohereAI plugin | Steampipe Hub

Name	Type	Description
_ctx	jsonb	Steampipe context in JSON form, e.g. connection_name.
classification	text	The classification results for the given input text(s).
confidence	double precision	The confidence score of the classification.
examples	text	The example text classified.
id	text	The ID of the classification.
inputs	text	The input text that was classified.
labels	jsonb	The labels of the classification.
settings	jsonb	Settings is a JSONB object that accepts any of the classify API request parameters.

Related issues

640: README.md · defog/sqlcoder-7b-2 at main

### Details

Similarity score: 0.89 - [ ] [README.md · defog/sqlcoder-7b-2 at main](https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true) # README.md · defog/sqlcoder-7b-2 at main **DESCRIPTION:** ```yaml license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation ``` ## Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. ## Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png) ### Model Details #### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] #### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai) **URL:** [https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true](https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true) #### Suggested labels ####

160: sid321axn/tinyllama-text2sql-finetuned at main

### Details

Similarity score: 0.88 ## tiny-llama-text2sql ## safetensors - [ ] [sid321axn/tinyllama-text2sql-finetuned at main](https://huggingface.co/sid321axn/tinyllama-text2sql-finetuned/tree/main) ## adapter https://huggingface.co/sid321axn/tiny-llama-text2sql This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset. ```json { "_name_or_path": "PY007/TinyLlama-1.1B-Chat-v0.3", "architectures": [ "LlamaForCausalLM" ], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 5632, "max_position_embeddings": 2048, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 22, "num_key_value_heads": 4, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 10000.0, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.37.0.dev0", "use_cache": false, "vocab_size": 32003 } ```

625: unsloth/README.md at main · unslothai/unsloth

### Details

Similarity score: 0.88 - [ ] [unsloth/README.md at main · unslothai/unsloth](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1) # unsloth/README.md at main · unslothai/unsloth

### Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory! ![](https://i.ibb.co/sJ7RhGG/image-41.png)

## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## 🦥 Unsloth.ai News - 📣 [Gemma 7b](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) on 6T tokens now works. And [Gemma 2b notebook](https://colab.research.google.com/drive/15gGm7x_jTm017_Ic8e317tdIpDG53Mtu?usp=sharing) - 📣 Added [conversational notebooks](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) and [raw text notebooks](https://colab.research.google.com/drive/1bMOKOBzxQWUIGZBs_B0zm8pimuEnZdfM?usp=sharing) - 📣 [2x faster inference](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) added for all our models - 📣 [DPO support](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) is now included. [More info](#DPO) on DPO - 📣 We did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗Hugging Face and are in their official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth) - 📣 [Download models 4x faster](https://huggingface.co/collections/unsloth/) from 🤗Hugging Face. Eg: `unsloth/mistral-7b-bnb-4bit` ## 🔗 Links and Resources | Type | Links | | ------------------------------- | --------------------------------------- | | 📚 **Wiki & FAQ** | [Read Our Wiki](https://github.com/unslothai/unsloth/wiki) | | 📜 **Documentation** | [Read The Doc](https://github.com/unslothai/unsloth/tree/main#-documentation) | | 💾 **Installation** | [unsloth/README.md](https://github.com/unslothai/unsloth/tree/main#installation-instructions)| |

**Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)| | 🥇 **Benchmarking** | [Performance Tables](https://github.com/unslothai/unsloth/tree/main#-performance-benchmarking) | 🌐 **Released Models** | [Unsloth Releases](https://huggingface.co/unsloth)| | ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)| ## ⭐ Key Features - All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**. - **0% loss in accuracy** - no approximation methods - all exact. - No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow. - Works on **Linux** and **Windows** via WSL. - Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes). - Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for **30x faster training**! - If you trained a model with 🦥Unsloth, you can use this cool sticker!

## 🥇 Performance Benchmarking - For the full list of **reproducable** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables) | 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) | |--------------|--------------|-----------------|---------------------|-----------------| | Alpaca | 1x | 1.04x | 1.98x | **15.64x** | | LAION Chip2 | 1x | 0.92x | 1.61x | **20.73x** | | OASST | 1x | 1.19x | 2.17x | **14.83x** | | Slim Orca | 1x | 1.18x | 2.22x | **14.82x** | - Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl). | Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction | | --- | --- | --- | --- | --- | --- | | Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% | | Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% | | Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% | | DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% | ![](https://i.ibb.co/sJ7RhGG/image-41.png) [View on GitHub](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1) #### Suggested labels ####

386: SciPhi/AgentSearch-V1 · Datasets at Hugging Face

### Details

Similarity score: 0.87 - [ ] [SciPhi/AgentSearch-V1 · Datasets at Hugging Face](https://huggingface.co/datasets/SciPhi/AgentSearch-V1) #### Getting Started The AgentSearch-V1 dataset is a comprehensive collection of over one billion embeddings, produced using jina-v2-base. It includes more than 50 million high-quality documents and over 1 billion passages, covering a vast range of content from sources such as Arxiv, Wikipedia, Project Gutenberg, and includes carefully filtered Creative Commons (CC) data. Our team is dedicated to continuously expanding and enhancing this corpus to improve the search experience. We welcome your thoughts and suggestions – please feel free to reach out with your ideas! To access and utilize the AgentSearch-V1 dataset, you can stream it via HuggingFace with the following Python code: ```python from datasets import load_dataset import json import numpy as np # To stream the entire dataset: ds = load_dataset("SciPhi/AgentSearch-V1", data_files="**/*", split="train", streaming=True) # Optional, stream just the "arxiv" dataset # ds = load_dataset("SciPhi/AgentSearch-V1", data_files="**/*", split="train", data_files="arxiv/*", streaming=True) # To process the entries: for entry in ds: embeddings = np.frombuffer( entry['embeddings'], dtype=np.float32 ).reshape(-1, 768) text_chunks = json.loads(entry['text_chunks']) metadata = json.loads(entry['metadata']) print(f'Embeddings:\n{embeddings}\n\nChunks:\n{text_chunks}\n\nMetadata:\n{metadata}') break ``` A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi-AI/agent-search/tree/main/scripts). Further, you may check the docs for details on how to perform RAG over AgentSearch. #### Languages English. #### Dataset Structure The raw dataset structure is as follows: ```json { "url": ..., "title": ..., "metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "..."}, "text_chunks": ..., "embeddings": ..., "dataset": "book" | "arxiv" | "wikipedia" | "stack-exchange" | "open-math" | "RedPajama-Data-V2" } ``` #### Dataset Creation This dataset was created as a step towards making humanities most important knowledge openly searchable and LLM optimal. It was created by filtering, cleaning, and augmenting locally publicly available datasets. To cite our work, please use the following: @software{SciPhi2023AgentSearch, author = {SciPhi}, title = {AgentSearch [ΨΦ]: A Comprehensive Agent-First Framework and Dataset for Webscale Search}, year = {2023}, url = {https://github.com/SciPhi-AI/agent-search} } #### Source Data @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } @misc{paster2023openwebmath, title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba}, year={2023}, eprint={2310.06786}, archivePrefix={arXiv}, primaryClass={cs.AI} } @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } #### License Please refer to the licenses of the data subsets you use. - Open-Web ([Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/)) - Books: the\_pile\_books3 license and pg19 license - ArXiv Terms of Use - Wikipedia License - StackExchange license on the Internet Archive #### Suggested labels #### { "key": "knowledge-dataset", "value": "A dataset with one billion embeddings from various sources, such as Arxiv, Wikipedia, Project Gutenberg, and carefully filtered Creative Commons data" }

396: astra-assistants-api: A backend implementation of the OpenAI beta Assistants API

### Details

Similarity score: 0.86 - [ ] [datastax/astra-assistants-api: A backend implementation of the OpenAI beta Assistants API](https://github.com/datastax/astra-assistants-api) Astra Assistant API Service ============================= A drop-in compatible service for the OpenAI beta Assistants API with support for persistent threads, files, assistants, messages, retrieval, function calling and more using AstraDB (DataStax's db as a service offering powered by Apache Cassandra and jvector). Compatible with existing OpenAI apps via the OpenAI SDKs by changing a single line of code. Getting Started --------------- 1. **Create an Astra DB Vector database** 2. Replace the following code: ```python client = OpenAI( api_key=OPENAI_API_KEY, ) ``` with: ```python client = OpenAI( base_url="https://open-assistant-ai.astra.datastax.com/v1", api_key=OPENAI_API_KEY, default_headers={ "astra-api-token": ASTRA_DB_APPLICATION_TOKEN, } ) ``` Or, if you have an existing astra db, you can pass your db\_id in a second header: ```python client = OpenAI( base_url="https://open-assistant-ai.astra.datastax.com/v1", api_key=OPENAI_API_KEY, default_headers={ "astra-api-token": ASTRA_DB_APPLICATION_TOKEN, "astra-db-id": ASTRA_DB_ID } ) ``` 3. **Create an assistant** ```python assistant = client.beta.assistants.create( instructions="You are a personal math tutor. When asked a math question, write and run code to answer the question.", model="gpt-4-1106-preview", tools=[{"type": "retrieval"}] ) ``` By default, the service uses AstraDB as the database/vector store and OpenAI for embeddings and chat completion. Third party LLM Support ----------------------- We now support many third party models for both embeddings and completion thanks to litellm. Pass the api key of your service using `api-key` and `embedding-model` headers. For AWS Bedrock, you can pass additional custom headers: ```python client = OpenAI( base_url="https://open-assistant-ai.astra.datastax.com/v1", api_key="NONE", default_headers={ "astra-api-token": ASTRA_DB_APPLICATION_TOKEN, "embedding-model": "amazon.titan-embed-text-v1", "LLM-PARAM-aws-access-key-id": BEDROCK_AWS_ACCESS_KEY_ID, "LLM-PARAM-aws-secret-access-key": BEDROCK_AWS_SECRET_ACCESS_KEY, "LLM-PARAM-aws-region-name": BEDROCK_AWS_REGION, } ) ``` and again, specify the custom model for the assistant. ```python assistant = client.beta.assistants.create( name="Math Tutor", instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.", model="meta.llama2-13b-chat-v1", ) ``` Additional examples including third party LLMs (bedrock, cohere, perplexity, etc.) can be found under `examples`. To run the examples using poetry: 1. Create a `.env` file in this directory with your secrets. 2. Run: ```shell poetry install poetry run python examples/completion/basic.py poetry run python examples/retreival/basic.py poetry run python examples/function-calling/basic.py ``` ### Coverage See our coverage report [here](your-coverage-report-link). ### Roadmap - Support for other embedding models and LLMs - Function calling - Pluggable RAG strategies - Streaming support #### Suggested labels #### { "key": "llm-function-calling", "value": "Integration of function calling with Large Language Models (LLMs)" }

626: classifiers/README.md at main · blockentropy/classifiers

### Details

Similarity score: 0.85 - [ ] [classifiers/README.md at main · blockentropy/classifiers](https://github.com/blockentropy/classifiers/blob/main/README.md?plain=1) # classifiers/README.md ## Fast Classifiers for Prompt Routing Routing and controlling the information flow is a core component in optimizing machine learning tasks. While some architectures focus on internal routing of data within a model, we focus on the external routing of data between models. This enables the combination of open source, proprietary, API based, and software based approaches to work together behind a smart router. We investigate three different ways of externally routing the prompt - cosine similarity via embeddings, zero-shot classification, and small classifiers. ## Implementation of Fast Classifiers The `code-class.ipynb` Jupyter notebook walks through the process of creating a fast prompt classifier for smart routing. For the fast classifiers, we utilize the model [DistilBERT](https://huggingface.co/docs/transformers/en/model_doc/distilbert), a smaller language representation model designed for efficient on-the-edge operation and training under computational constraints. DistilBERT is not only less costly to pre-train but also well-suited for on-device computations, as demonstrated through experiments and comparative studies. We quantize the model using [Optimum](https://huggingface.co/docs/optimum/index), enabling the model to run extremely fast on a CPU router. Each classifier takes 5-8ms to run. An ensemble of 8 prompt classifiers takes about 50ms in total. Thus, each endpoint can route about 20 requests per second. In the example `code-class`, we are deciding between prompts of code and not code prompts. The two datasets used are the 52K [instruction-following data](https://arxiv.org/abs/2304.03277) generated by GPT-4 with prompts in Alpaca. And the 20K instruction-following data used for fine-tuning the [Code Alpaca](https://github.com/sahil280114/codealpaca) model. Train test split of 80/20 yields an accuracy of 95.49% and f1 score of 0.9227. ![Train Test](./traintest.png) ## Comparison vs other Routing methods The most popular alternative to routing is via embedding similarity. For example, if one were to try to route a programming question, one might set up the set of target classes as ["coding", "not coding"]. Each one of these strings is then transformed into an embedding and compared against a prompt query like, "write a bubble sort in python". Given the computed pair-wise cosine similarity between the query and class, we can then label the prompt as a coding question and route the prompt to a coding-specific model. These do not scale well with larger numbers of embeddings. Nor are they able to capture non-semantic type classes (like is the response likely to be more or less than 200 tokens). However, they are adaptable and comparably fast and thus provide a good alternative to the trained fast classifiers. ![Train Test](./graphs.png) Quantifying different methods of routing in terms of execution time. As the prompt size increases, the query time also increases as shown in (a). There is also a close to linear increase in the time as the number of classes increase as shown in (b). However, the small classifiers do not increase in time as the class examples increase in the number of tokens (c). This is due to the upfront cost of training the binary classifier, reducing cost at inference. ## Reproducibility The `timing_tests.js` and `complexity.js` files can be used for reproducibility. Note that only the code classifier is currently available in this repo. One will need to install the appropriate models from the [Transformers.js](https://huggingface.co/docs/transformers.js/en/index) repo. [View on GitHub](https://github.com/blockentropy/classifiers/blob/main/README.md?plain=1) #### Suggested labels #### {'label-name': 'Prompt-Routing', 'label-description': 'Focuses on external routing of data between models to optimize machine learning tasks.', 'confidence': 50.24}

irthomasthomas / undecidability