GGUF My Repo
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Create quantized models from Hugging Face repos
Inflammable1230
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sergiopaniego's
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The Christmas holidays are here! ๐
Thinking about learning something new in AI?
@huggingface offers 12 FREE courses covering all the relevant topics, for every level of experience. A great challenge for the holidays (and worth saving for later ๐)
Letโs explore them!
๐ง ๐๐๐ ๐๐ผ๐๐ฟ๐๐ฒ: large language models with HF tools
https://huggingface.co/learn/llm-course
๐ค ๐๐ด๐ฒ๐ป๐๐ ๐๐ผ๐๐ฟ๐๐ฒ: build and deploy AI agents
https://huggingface.co/learn/agents-course
๐จ ๐๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ: diffusion models with ๐ค Diffusers
https://huggingface.co/learn/diffusion-course
๐ ๐๐๐ฑ๐ถ๐ผ ๐๐ผ๐๐ฟ๐๐ฒ: transformers for audio tasks
https://huggingface.co/learn/audio-course
๐ฎ ๐๐ฒ๐ฒ๐ฝ ๐ฅ๐ ๐๐ผ๐๐ฟ๐๐ฒ: deep reinforcement learning
https://huggingface.co/learn/deep-rl-course
๐๏ธ ๐๐ผ๐บ๐บ๐๐ป๐ถ๐๐ ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ: modern computer vision with HF
https://huggingface.co/learn/computer-vision-course
๐ฆพ ๐ฅ๐ผ๐ฏ๐ผ๐๐ถ๐ฐ๐ ๐๐ผ๐๐ฟ๐๐ฒ (๐๐ฒ๐ฅ๐ผ๐ฏ๐ผ๐): learning-based robotics
https://huggingface.co/learn/robotics-course
๐งฉ ๐ ๐๐ฃ ๐๐ผ๐๐ฟ๐๐ฒ: Model Context Protocol explained
https://huggingface.co/learn/mcp-course
๐งช ๐ ๐ฆ๐บ๐ผ๐น ๐๐ผ๐๐ฟ๐๐ฒ: post-training AI models
https://huggingface.co/learn/a-smol-course
๐น๏ธ ๐ ๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐บ๐ฒ๐: AI in game development
https://huggingface.co/learn/ml-for-games-course
๐ง ๐ ๐ ๐ณ๐ผ๐ฟ ๐ฏ๐: machine learning for 3D data
https://huggingface.co/learn/ml-for-3d-course
๐ ๐ข๐ฝ๐ฒ๐ป-๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ ๐๐ผ๐ผ๐ธ๐ฏ๐ผ๐ผ๐ธ: practical AI notebooks
https://huggingface.co/learn/cookbook
All of them can be found here: https://huggingface.co/learn
Thinking about learning something new in AI?
@huggingface offers 12 FREE courses covering all the relevant topics, for every level of experience. A great challenge for the holidays (and worth saving for later ๐)
Letโs explore them!
๐ง ๐๐๐ ๐๐ผ๐๐ฟ๐๐ฒ: large language models with HF tools
https://huggingface.co/learn/llm-course
๐ค ๐๐ด๐ฒ๐ป๐๐ ๐๐ผ๐๐ฟ๐๐ฒ: build and deploy AI agents
https://huggingface.co/learn/agents-course
๐จ ๐๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ: diffusion models with ๐ค Diffusers
https://huggingface.co/learn/diffusion-course
๐ ๐๐๐ฑ๐ถ๐ผ ๐๐ผ๐๐ฟ๐๐ฒ: transformers for audio tasks
https://huggingface.co/learn/audio-course
๐ฎ ๐๐ฒ๐ฒ๐ฝ ๐ฅ๐ ๐๐ผ๐๐ฟ๐๐ฒ: deep reinforcement learning
https://huggingface.co/learn/deep-rl-course
๐๏ธ ๐๐ผ๐บ๐บ๐๐ป๐ถ๐๐ ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ: modern computer vision with HF
https://huggingface.co/learn/computer-vision-course
๐ฆพ ๐ฅ๐ผ๐ฏ๐ผ๐๐ถ๐ฐ๐ ๐๐ผ๐๐ฟ๐๐ฒ (๐๐ฒ๐ฅ๐ผ๐ฏ๐ผ๐): learning-based robotics
https://huggingface.co/learn/robotics-course
๐งฉ ๐ ๐๐ฃ ๐๐ผ๐๐ฟ๐๐ฒ: Model Context Protocol explained
https://huggingface.co/learn/mcp-course
๐งช ๐ ๐ฆ๐บ๐ผ๐น ๐๐ผ๐๐ฟ๐๐ฒ: post-training AI models
https://huggingface.co/learn/a-smol-course
๐น๏ธ ๐ ๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐บ๐ฒ๐: AI in game development
https://huggingface.co/learn/ml-for-games-course
๐ง ๐ ๐ ๐ณ๐ผ๐ฟ ๐ฏ๐: machine learning for 3D data
https://huggingface.co/learn/ml-for-3d-course
๐ ๐ข๐ฝ๐ฒ๐ป-๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ ๐๐ผ๐ผ๐ธ๐ฏ๐ผ๐ผ๐ธ: practical AI notebooks
https://huggingface.co/learn/cookbook
All of them can be found here: https://huggingface.co/learn
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๐ SignRoundV2 for LLM quantization: PTQ-level cost, QAT-level accuracy โ yes, even at 2 bits.
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs (2512.04746)
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs (2512.04746)
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singhsidhukuldeep's
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29 days ago
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4014
Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).
Here are the steps on how Logic-of-Thought (LoT) is implemented:
-- 1. Logic Extraction
1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (ยฌ) for propositions that express opposite meanings.
7. Use implication (โ) to connect propositional symbols when a conditional relationship exists.
-- 2. Logic Extension
1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
-- 3. Logic Translation
1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.
-- 4. Integration with Existing Prompting Methods
1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.
What do you think about LoT?
Here are the steps on how Logic-of-Thought (LoT) is implemented:
-- 1. Logic Extraction
1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (ยฌ) for propositions that express opposite meanings.
7. Use implication (โ) to connect propositional symbols when a conditional relationship exists.
-- 2. Logic Extension
1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
-- 3. Logic Translation
1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.
-- 4. Integration with Existing Prompting Methods
1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.
What do you think about LoT?
- 2 replies