WizardLM
quix-streams
WizardLM | quix-streams | |
---|---|---|
38 | 25 | |
7,531 | 796 | |
- | 50.8% | |
9.4 | 9.0 | |
8 months ago | 6 days ago | |
Python | Python | |
- | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
WizardLM
- FLaNK AI-April 22, 2024
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Refact LLM: New 1.6B code model reaches 32% HumanEval and is SOTA for the size
This is interesting work, and a good contribution, but there is no need to mislead people.
[1] https://github.com/nlpxucan/WizardLM
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Continue with LocalAI: An alternative to GitHub's Copilot that runs everything locally
If you pair this with the latest WizardCoder models, which have a fairly better performance than the standard Salesforce Codegen2 and Codegen2.5, you have a pretty solid alternative to GitHub Copilot that runs completely locally.
- WizardCoder context?
- The world's most-powerful AI model suddenly got 'lazier' and 'dumber.' A radical redesign of OpenAI's GPT-4 could be behind the decline in performance.
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Official WizardLM-13B-V1.1 Released! Train with Only 1K Data! Can Achieve 86.32% on AlpacaEval!
(We will update the demo links in our github.)
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GPT-4 API general availability
In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.
You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.
That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).
I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)
[1] https://github.com/turboderp/exllama#results-so-far
[2] https://github.com/aigoopy/llm-jeopardy
[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...
[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
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WizardLM-13B-V1.0-Uncensored
You talking about this? https://github.com/nlpxucan/WizardLM
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What 7b llm to use
The smallest model that is close to competent at code is WizardCoder 15B.. https://github.com/nlpxucan/WizardLM/
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16-Jun-2023
WizardCoder: Empowering Code Large Language Models with Evol-Instruct (https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder)
quix-streams
- Show HN: Streaming DataFrames–a Pandas-like syntax for real-time data
- FLaNK AI-April 22, 2024
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
Airflow for Streaming
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Apache Pulsar VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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redpanda VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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ApacheKafka VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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flink-statefun VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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beam VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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debezium VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
Debezium is a change data capture framework for streaming. Connect it to your databases to detect changes and produce those change events to Kafka. Quix Streams is a Python stream processing library for ML and AI applications. It builds on the Confluent Python library to add a state store with RocksDB and adds a Streaming DataFrames API with declarative operations like Windows. It is designed for analytics and data engineering workloads. Use it together with Debezium to process your CDC data in real-time.
What are some alternatives?
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
confluent-kafka-python - Confluent's Kafka Python Client
llm-humaneval-benchmarks
aiokafka - asyncio client for kafka
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
quix-samples - Library samples repository of Quix. Explore and Deploy them easily on https://portal.platform.quix.ai
airoboros - Customizable implementation of the self-instruct paper.
qr-code - A no-framework, no-dependencies, customizable, animate-able, SVG-based <qr-code> HTML element.
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
bytewax - Python Stream Processing
can-ai-code - Self-evaluating interview for AI coders
csv-import - The open-source CSV importer, maintained by @tableflowhq