Retrieval Augmented Generation Optimised LLM's

Hacker News

I'm super excited to show you newly published DocsGPT llm’s on Hugging Face, tailor-made for tasks some of you asked for. From Documentation-based QA, RAG (Retrieval Augmented Generation) to assisting developers and tech support teams by conversing with your data! (basically the same thing tbh, all started by 2020 Retrieval Augmented Generation for Knowledge-Intensive NLP Tasks paper) Fine-tuned with 50k high-quality examples using the Lora process! Took around 2 days for smaller ones and 4 for a large one, 2 epochs each. Check them out: DocsGPT-7b-falcon DocsGPT-14b DocsGPT-40b-falcon Why I think its useful? Improved explicit info extraction from sources Reduced hallucinations No repeating at the end Name Base Model Requirements (or similar) GPU Docsgpt-7b-falcon Falcon-7b 1xA10G Docsgpt-14b llama-2-13b-hf 2xA10 Docsgpt-40b-falcon falcon-40b 8xA10G You can also use bitsnbytes to run the with less memory A snippet to jumpstart: python model = "Arc53/docsgpt-7b-falcon" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) License? Apache-2.0 Will publish gglm versions if you guys like them, im also hoping a can tune a nice 3b sized model in future too.

Visit product View on Hacker NewsAug 17, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, tasks · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, llama, pipe · Missing: https docs, just released, exist

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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