mistralai/Mistral-Small-24B-Instruct-2501
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Fabio Ferrua
Marvin73
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Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth
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Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth
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๐ต๐ป ๐๐ ๐๐ง๐ญ๐ข๐ ๐๐๐ ๐ฐ๐ข๐ญ๐ก ๐ฆ ๐๐ฅ๐๐ฆ๐ 3.2
I was excited to explore Llama 3.2, but as a simple ๐ช๐บ EU guy, I don't have access to Meta's multimodal models ๐ฟ
๐ค So I thought: why not challenge the small 3B text model with Agentic RAG?
๐ฏ The plan:
- Build a system that tries to answer questions using a knowledge base.
- If the documents don't contain the answer, use Web search for additional context.
Check out my experimental notebook here: ๐ https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/llama32_agentic_rag.ipynb
My stack:
๐๏ธ haystack (https://haystack.deepset.ai/): open-source LLM orchestration framework
๐ฆ meta-llama/Llama-3.2-3B-Instruct
๐ฆ๐ free DuckDuckGo API, integrated with Haystack
โจ ๐๐ฉ๐ฆ ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต๐ด? ๐๐ฏ๐ค๐ฐ๐ถ๐ณ๐ข๐จ๐ช๐ฏ๐จ - ๐ข ๐ง๐ฆ๐ธ ๐ฎ๐ฐ๐ฏ๐ต๐ฉ๐ด ๐ข๐จ๐ฐ, ๐ต๐ฉ๐ช๐ด ๐ญ๐ฆ๐ท๐ฆ๐ญ ๐ฐ๐ง ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ ๐ง๐ณ๐ฐ๐ฎ ๐ข ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ธ๐ฐ๐ถ๐ญ๐ฅ'๐ท๐ฆ ๐ฃ๐ฆ๐ฆ๐ฏ ๐ถ๐ฏ๐ต๐ฉ๐ช๐ฏ๐ฌ๐ข๐ฃ๐ญ๐ฆ!
This probably reflects the impressive IFEval score of the model (comparable to Llama 3.1 8B).
I was excited to explore Llama 3.2, but as a simple ๐ช๐บ EU guy, I don't have access to Meta's multimodal models ๐ฟ
๐ค So I thought: why not challenge the small 3B text model with Agentic RAG?
๐ฏ The plan:
- Build a system that tries to answer questions using a knowledge base.
- If the documents don't contain the answer, use Web search for additional context.
Check out my experimental notebook here: ๐ https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/llama32_agentic_rag.ipynb
My stack:
๐๏ธ haystack (https://haystack.deepset.ai/): open-source LLM orchestration framework
๐ฆ meta-llama/Llama-3.2-3B-Instruct
๐ฆ๐ free DuckDuckGo API, integrated with Haystack
โจ ๐๐ฉ๐ฆ ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต๐ด? ๐๐ฏ๐ค๐ฐ๐ถ๐ณ๐ข๐จ๐ช๐ฏ๐จ - ๐ข ๐ง๐ฆ๐ธ ๐ฎ๐ฐ๐ฏ๐ต๐ฉ๐ด ๐ข๐จ๐ฐ, ๐ต๐ฉ๐ช๐ด ๐ญ๐ฆ๐ท๐ฆ๐ญ ๐ฐ๐ง ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ ๐ง๐ณ๐ฐ๐ฎ ๐ข ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ธ๐ฐ๐ถ๐ญ๐ฅ'๐ท๐ฆ ๐ฃ๐ฆ๐ฆ๐ฏ ๐ถ๐ฏ๐ต๐ฉ๐ช๐ฏ๐ฌ๐ข๐ฃ๐ญ๐ฆ!
This probably reflects the impressive IFEval score of the model (comparable to Llama 3.1 8B).
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I'm excited to announce that Transformers.js V3 is finally available on NPM! ๐ฅ State-of-the-art Machine Learning for the web, now with WebGPU support! ๐คฏโก๏ธ
Install it from NPM with:
๐๐๐ ๐ @๐๐๐๐๐๐๐๐๐๐๐/๐๐๐๐๐๐๐๐๐๐๐๐
or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q
Segment Anything demo: webml-community/segment-anything-webgpu
Install it from NPM with:
๐๐๐ ๐ @๐๐๐๐๐๐๐๐๐๐๐/๐๐๐๐๐๐๐๐๐๐๐๐
or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q
Segment Anything demo: webml-community/segment-anything-webgpu
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๐จExciting news for the Multilingual Synthetic Data Community!๐จ
Iโve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Hereโs whatโs new!
๐ The MAGPIE paper showcased that if you use the instruction-tuned version (
๐ค While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?
๐ And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.
๐ฉโ๐ป To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using
[Script](https://gist.github.com/mrm8488/4650a5e3cc45523798a527a3446eb312)
๐ Explore the datasets ๐ generated using our new script!
- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
- [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)
Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.
Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/
Iโve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Hereโs whatโs new!
๐ The MAGPIE paper showcased that if you use the instruction-tuned version (
Llama-3-8B-instruct) to generate synthetic instructions and then fine-tune the base version (Llama-3-8B) on this dataset, you can improve even the it-tuned version๐ค While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?
๐ And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.
๐ฉโ๐ป To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using
ollama models (initially phi and llama3) automatically and upload it to the Hugging Face Hub
๐ Explore the datasets ๐ generated using our new script!
- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
- [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)
Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.
Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/
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๐งช RAG Evaluation with ๐ฅ Prometheus 2 + Haystack
๐ Blog post: https://haystack.deepset.ai/blog/rag-evaluation-with-prometheus-2
๐ Notebook: https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prometheus2_evaluation.ipynb
โโโ โโ โโ โ โโโ
When evaluating LLMs' responses, ๐ฉ๐ซ๐จ๐ฉ๐ซ๐ข๐๐ญ๐๐ซ๐ฒ ๐ฆ๐จ๐๐๐ฅ๐ฌ like GPT-4 are commonly used due to their strong performance.
However, relying on closed models presents challenges related to data privacy ๐, transparency, controllability, and cost ๐ธ.
On the other hand, ๐จ๐ฉ๐๐ง ๐ฆ๐จ๐๐๐ฅ๐ฌ typically do not correlate well with human judgments and lack flexibility.
๐ฅ Prometheus 2 is a new family of open-source models designed to address these gaps:
๐น two variants: prometheus-eval/prometheus-7b-v2.0; prometheus-eval/prometheus-8x7b-v2.0
๐น trained on open-source data
๐น high correlation with human evaluations and proprietary models
๐น highly flexible: capable of performing direct assessments and pairwise rankings, and allowing the definition of custom evaluation criteria.
See my experiments with RAG evaluation in the links above.
๐ Blog post: https://haystack.deepset.ai/blog/rag-evaluation-with-prometheus-2
๐ Notebook: https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prometheus2_evaluation.ipynb
โโโ โโ โโ โ โโโ
When evaluating LLMs' responses, ๐ฉ๐ซ๐จ๐ฉ๐ซ๐ข๐๐ญ๐๐ซ๐ฒ ๐ฆ๐จ๐๐๐ฅ๐ฌ like GPT-4 are commonly used due to their strong performance.
However, relying on closed models presents challenges related to data privacy ๐, transparency, controllability, and cost ๐ธ.
On the other hand, ๐จ๐ฉ๐๐ง ๐ฆ๐จ๐๐๐ฅ๐ฌ typically do not correlate well with human judgments and lack flexibility.
๐ฅ Prometheus 2 is a new family of open-source models designed to address these gaps:
๐น two variants: prometheus-eval/prometheus-7b-v2.0; prometheus-eval/prometheus-8x7b-v2.0
๐น trained on open-source data
๐น high correlation with human evaluations and proprietary models
๐น highly flexible: capable of performing direct assessments and pairwise rankings, and allowing the definition of custom evaluation criteria.
See my experiments with RAG evaluation in the links above.
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