txtinstruct
instruct-eval
| txtinstruct | instruct-eval | |
|---|---|---|
| 13 | 6 | |
| 215 | 471 | |
| 2.8% | 4.0% | |
| 5.0 | 8.0 | |
| 8 months ago | 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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txtinstruct
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Questions about memory, tree-of-thought, planning
I tried cromadb but had terrible performance and could not pin down the cause (likely a problem on my end). Weaviate was easy to setup and had excellent performance, this is probably what I will use in the future. Next on my list is txtinstruct, to finetune a model with data that does not change and using a vector db for everything else seems promising.
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[R] Let Language Models be Language Models
The closest thing I've seen to this is txtinstruct
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Create a ChatGPT-like program using an open source model and custom data.
txtinstruct is a framework for training instruction-tuned models
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Stability AI Launches the First of Its StableLM Suite of Language Models
Great to see the continued release of open models. The only disappointing thing is that models keep building on CC-BY-NC licensed datasets, which severely limits their use.
Hopefully, people consider txtinstruct (https://github.com/neuml/txtinstruct) and other approaches to generate instruction-tuning datasets without the baggage.
- Build open instruction-tuned datasets and models (r/MachineLearning)
- Build open instruction-tuned datasets and models
- [P] Build open instruction-tuned datasets and models
- Create open instruction-tuned datasets and LLM models
- Show HN: Build open instruction-tuned datasets and models
instruct-eval
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Eval mmlu result against various infer methods (HF_Causal, VLLM, AutoGPTQ, AutoGPTQ-exllama)
I modified declare-lab's instruct-eval scripts, add support to VLLM, AutoGPTQ (and new autoGPTQ support exllama now), and test the mmlu result. I also add support to fastllm (which can accelerate ChatGLM2-6b.The code is here https://github.com/declare-lab/instruct-eval , I'd like to hear any errors in those code.
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[D] Red Pajamas Instruct 7B. Is it really that bad or some some ggml/quantization artifact? Vicuna-7b has no issue writing stories and even does basic text transformation. Yet RP refuses to do anything most of the time. It does generate a story if you run it as a raw model, but gets into a loop.
Well, I ran it with exactly the same parameters I ran Vicuna 7b, although I ran Vicuna with llama.cpp. while PJ can only be ran with ggml (I don't have a GPU). And Vicuna looped only when temperature reached 0. Given how hard it loops, I think it is some bug with ggml. Testers claim it should be close to 7b alpaca/vicuna:https://github.com/declare-lab/flan-eval
- [P] The first RedPajama models are here! The 3B and 7B models are now available under Apache 2.0, including instruction-tuned and chat versions. These models aim replicate LLaMA as closely as possible.
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Best Instruct-Trained Alternative to Alpaca/Vicuna?
For a list of other instruction tuned models, you can check out this benchmark here: https://github.com/declare-lab/flan-eval
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[R]Comprehensive List of Instruction Datasets for Training LLM Models (GPT-4 & Beyond)
Great resource! I’ve recently also benchmarked many of the popular instruction models here: https://github.com/declare-lab/flan-eval
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Stability AI Launches the First of Its StableLM Suite of Language Models
I really dislike this approach of announcing new models that some companies have taken, they don't mention evaluation results or performance of the model, but instead talk about how "transparent", "accessible" and "supportive" these models are.
Anyway, I have benchmarked stablelm-base-alpha-3b (the open-source version, not the fine-tuned one which is under a NC license) using the MMLU benchmark and the results are rather underwhelming compared to other open source models:
* stablelm-base-alpha-3b (3B params): 25.6% average accuracy
* flan-t5-xl (3B params): 49.3% average accuracy
* flan-t5-small (80M params): 29.4% average accuracy
MMLU is just one benchmark, but based on the blog post, I don't think it will yield much better results in others. I'll leave links to the MMLU results of other proprietary[0] and open-access[1] models (results may vary by ±2% depending on the parameters used during inference).
[0]: https://paperswithcode.com/sota/multi-task-language-understa...
[1]: https://github.com/declare-lab/flan-eval/blob/main/mmlu.py#L...
What are some alternatives?
StableLM - StableLM: Stability AI Language Models
lm-evaluation-harness - A framework for few-shot evaluation of autoregressive language models.
safetensors - Simple, safe way to store and distribute tensors
AlpacaDataCleaned - Alpaca dataset from Stanford, cleaned and curated
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
geov - The GeoV model is a large langauge model designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER). We have shared a pre-trained 9B parameter model.
cataclysm - Cataclysm - Code generation library for the end game
Emu - Emu Series: Generative Multimodal Models from BAAI
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models