cohereai_classify table | CohereAI plugin | Steampipe Hub #644
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Related issues#640: README.md · defog/sqlcoder-7b-2 at main### DetailsSimilarity 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 mainDESCRIPTION: license: cc-by-sa-4.0
library_name: transformers
pipeline_tag: text-generationUpdate noticeThe 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-2A capable large language model for natural language to SQL generation. Model DetailsModel DescriptionThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Model Sources [optional]UsesThis 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 ModelUse the code here to get started with the model. PromptPlease use the following prompt for optimal results. Please remember to use EvaluationThis model was evaluated on 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. ResultsWe 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.
Model Card ContactContact us on X at @defogdata, or on email at founders@defog.ai URL: https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true Suggested labels#160: sid321axn/tinyllama-text2sql-finetuned at main### DetailsSimilarity score: 0.88 ## tiny-llama-text2sql ## safetensors - [ ] [sid321axn/tinyllama-text2sql-finetuned at main](https://huggingface.co/sid321axn/tinyllama-text2sql-finetuned/tree/main)adapterhttps://huggingface.co/sid321axn/tiny-llama-text2sql This model is a fine-tuned version of PY007/TinyLlama-1.1B-Chat-v0.3 on the None dataset. {
"_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
}
```</details>
### #625: unsloth/README.md at main · unslothai/unsloth
<details><summary>### Details</summary>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
<div align="center">
<a href="https://unsloth.ai"><picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
<img alt="unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="110" style="max-width: 100%;">
</picture></a>
<a href="https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/start free finetune button.png" height="48"></a>
<a href="https://discord.gg/u54VK8m8tk"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
<a href="https://ko-fi.com/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy me a coffee button.png" height="48"></a>
### Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory!
</div>
## ✨ 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)|
| <img height="14" src="https://upload.wikimedia.org/wikipedia/commons/6/6f/Logo_of_Twitter.svg" /> **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! <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" height="50" align="center" />
## 🥇 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% |
[View on GitHub](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1)
#### Suggested labels
#### </details>
### #386: SciPhi/AgentSearch-V1 · Datasets at Hugging Face
<details><summary>### Details</summary>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}')
breakA full set of scripts to recreate the dataset from scratch can be found here. Further, you may check the docs for details on how to perform RAG over AgentSearch. LanguagesEnglish. Dataset StructureThe raw dataset structure is as follows: {
"url": ...,
"title": ...,
"metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "..."},
"text_chunks": ...,
"embeddings": ...,
"dataset": "book" | "arxiv" | "wikipedia" | "stack-exchange" | "open-math" | "RedPajama-Data-V2"
}Dataset CreationThis 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, Source Data@online{wikidump, @misc{paster2023openwebmath, @software{together2023redpajama, LicensePlease refer to the licenses of the data subsets you use.
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### DetailsSimilarity 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 ServiceA 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
client = OpenAI(
api_key=OPENAI_API_KEY,
)with: 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: 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
}
)
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 SupportWe now support many third party models for both embeddings and completion thanks to litellm. Pass the api key of your service using For AWS Bedrock, you can pass additional custom headers: 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. 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 To run the examples using poetry:
poetry install
poetry run python examples/completion/basic.py
poetry run python examples/retreival/basic.py
poetry run python examples/function-calling/basic.pyCoverageSee our coverage report here. Roadmap
Suggested labels{ "key": "llm-function-calling", "value": "Integration of function calling with Large Language Models (LLMs)" }#626: classifiers/README.md at main · blockentropy/classifiers### DetailsSimilarity score: 0.85 - [ ] [classifiers/README.md at main · blockentropy/classifiers](https://github.com/blockentropy/classifiers/blob/main/README.md?plain=1)classifiers/README.mdFast Classifiers for Prompt RoutingRouting 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 ClassifiersThe We quantize the model using Optimum, 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 Train test split of 80/20 yields an accuracy of 95.49% and f1 score of 0.9227. Comparison vs other Routing methodsThe 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.
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. ReproducibilityThe Suggested labels{'label-name': 'Prompt-Routing', 'label-description': 'Focuses on external routing of data between models to optimize machine learning tasks.', 'confidence': 50.24} |
TITLE: cohereai_classify table | CohereAI plugin | Steampipe Hub
DESCRIPTION:
Overview
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GitHub
steampipe plugin install mr-destructive/cohereai
cohereai_classify
cohereai_detect_language
cohereai_detokenize
cohereai_embed
cohereai_generation
cohereai_summaraize
cohereai_summarize
cohereai_tokenize
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Table: cohereai_classify
Get classification for a given input strings and examples.
Notes:
Examples
Basic classification with given set of inputs and examples
Classification with specific settings(model, preset)
Email Spam Classification
Schema for cohereai_classify
URL: cohereai_classify table | CohereAI plugin | Steampipe Hub
Suggested labels
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