librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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Thireus/Qwen3-4B-Thinking-2507-THIREUS-Q5_K-SPECIAL_SPLIT	Thireus	2025-09-05T20:23:29Z	3	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-29T05:51:43Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
fopppyu/blockassist-bc-stinky_stinky_cassowary_1757103700	fopppyu	2025-09-05T20:22:41Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinky stinky cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:21:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinky stinky cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BrinnTano/jarvis-deepsearch	BrinnTano	2025-09-05T20:22:33Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-05T20:22:33Z	--- license: apache-2.0 ---
bah63843/blockassist-bc-plump_fast_antelope_1757103621	bah63843	2025-09-05T20:21:53Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:21:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ1_M-SPECIAL_SPLIT	Thireus	2025-09-05T20:21:46Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-29T05:56:38Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ5_K_R4-SPECIAL_SPLIT	Thireus	2025-09-05T20:21:11Z	3	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-29T05:54:11Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ4_XS-SPECIAL_SPLIT	Thireus	2025-09-05T20:20:42Z	3	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-29T05:54:18Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
seraphimzzzz/1614495	seraphimzzzz	2025-09-05T20:20:09Z	0	null	[ "region:us" ]	null	2025-09-05T20:20:09Z	[View on Civ Archive](https://civarchive.com/models/1380495?modelVersionId=1561100)
ultratopaz/1614478	ultratopaz	2025-09-05T20:19:59Z	0	null	[ "region:us" ]	null	2025-09-05T20:20:02Z	[View on Civ Archive](https://civarchive.com/models/1449650?modelVersionId=1639054)
Soya2305/ppo-LunarLander-v3	Soya2305	2025-09-05T20:19:48Z	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-09-05T19:54:24Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: 255.71 +/- 20.27 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v3 This is a trained model of a PPO agent playing LunarLander-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
crystalline7/1069413	crystalline7	2025-09-05T20:19:43Z	0	null	[ "region:us" ]	null	2025-09-05T20:19:46Z	[View on Civ Archive](https://civarchive.com/models/1037884?modelVersionId=1164190)
seraphimzzzz/1614485	seraphimzzzz	2025-09-05T20:19:31Z	0	null	[ "region:us" ]	null	2025-09-05T20:19:33Z	[View on Civ Archive](https://civarchive.com/models/1438138?modelVersionId=1625671)
amethyst9/1070492	amethyst9	2025-09-05T20:19:05Z	0	null	[ "region:us" ]	null	2025-09-05T20:19:08Z	[View on Civ Archive](https://civarchive.com/models/1038796?modelVersionId=1165262)
amethyst9/1167553	amethyst9	2025-09-05T20:18:37Z	0	null	[ "region:us" ]	null	2025-09-05T20:18:40Z	[View on Civ Archive](https://civarchive.com/models/1123223?modelVersionId=1262447)
crystalline7/1614476	crystalline7	2025-09-05T20:18:30Z	0	null	[ "region:us" ]	null	2025-09-05T20:18:33Z	[View on Civ Archive](https://civarchive.com/models/1437649?modelVersionId=1625115)
Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ2_KS-SPECIAL_SPLIT	Thireus	2025-09-05T20:18:24Z	3	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-28T23:55:46Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
crystalline7/1614504	crystalline7	2025-09-05T20:18:16Z	0	null	[ "region:us" ]	null	2025-09-05T20:18:19Z	[View on Civ Archive](https://civarchive.com/models/1038258?modelVersionId=1164624)
amethyst9/1614506	amethyst9	2025-09-05T20:18:12Z	0	null	[ "region:us" ]	null	2025-09-05T20:18:12Z	[View on Civ Archive](https://civarchive.com/models/1426394?modelVersionId=1612237)
crystalline7/983283	crystalline7	2025-09-05T20:18:02Z	0	null	[ "region:us" ]	null	2025-09-05T20:18:05Z	[View on Civ Archive](https://civarchive.com/models/963042?modelVersionId=1078217)
ultratopaz/1614479	ultratopaz	2025-09-05T20:17:46Z	0	null	[ "region:us" ]	null	2025-09-05T20:17:45Z	[View on Civ Archive](https://civarchive.com/models/1449650?modelVersionId=1639022)
ultratopaz/1614489	ultratopaz	2025-09-05T20:17:33Z	0	null	[ "region:us" ]	null	2025-09-05T20:17:33Z	[View on Civ Archive](https://civarchive.com/models/1380503?modelVersionId=1653825)
ultratopaz/1070360	ultratopaz	2025-09-05T20:17:15Z	0	null	[ "region:us" ]	null	2025-09-05T20:17:05Z	[View on Civ Archive](https://civarchive.com/models/1038694?modelVersionId=1165139)
seraphimzzzz/1614501	seraphimzzzz	2025-09-05T20:16:38Z	0	null	[ "region:us" ]	null	2025-09-05T20:16:36Z	[View on Civ Archive](https://civarchive.com/models/993331?modelVersionId=1112961)
ultratopaz/1614470	ultratopaz	2025-09-05T20:16:17Z	0	null	[ "region:us" ]	null	2025-09-05T20:16:20Z	[View on Civ Archive](https://civarchive.com/models/1069541?modelVersionId=1200489)
trongg/5a1f05b4-a01a-486d-b95c-ff2164c0b463	trongg	2025-09-05T20:16:01Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-05T19:48:56Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ1_M_R4-SPECIAL_SPLIT	Thireus	2025-09-05T20:15:38Z	3	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-28T23:42:34Z	--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
amethyst9/980180	amethyst9	2025-09-05T20:15:33Z	0	null	[ "region:us" ]	null	2025-09-05T20:15:32Z	[View on Civ Archive](https://civarchive.com/models/960215?modelVersionId=1075070)
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757103268	fakir22	2025-09-05T20:15:24Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:15:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757103250	bah63843	2025-09-05T20:15:05Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:14:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TrinitySkye/TrintySkye-Replicate	TrinitySkye	2025-09-05T20:14:54Z	2	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-04T17:17:05Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Trinity --- # Trintyskye Replicate <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Trinity` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Trinity", "lora_weights": "https://huggingface.co/TrinitySkye/TrintySkye-Replicate/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('TrinitySkye/TrintySkye-Replicate', weight_name='lora.safetensors') image = pipeline('Trinity').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2008 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/TrinitySkye/TrintySkye-Replicate/discussions) to add images that show off what you’ve made with this LoRA.
amethyst9/1475260	amethyst9	2025-09-05T20:14:52Z	0	null	[ "region:us" ]	null	2025-09-05T20:14:48Z	[View on Civ Archive](https://civarchive.com/models/1393873?modelVersionId=1575460)
Miracle-man/blockassist-bc-singing_lithe_koala_1757101522	Miracle-man	2025-09-05T20:14:43Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing lithe koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:14:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing lithe koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vennertou/blockassist-bc-durable_rangy_elephant_1757103204	vennertou	2025-09-05T20:14:11Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "durable rangy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:13:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - durable rangy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
grizzle00/blockassist-bc-curious_mimic_antelope_1757101009	grizzle00	2025-09-05T20:13:47Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "curious mimic antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:13:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - curious mimic antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/L3-8B-Lunaris-cl-i1-GGUF	mradermacher	2025-09-05T20:13:13Z	0	transformers	[ "transformers", "gguf", "en", "base_model:dinalad0/L3-8B-Lunaris-cl", "base_model:quantized:dinalad0/L3-8B-Lunaris-cl", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-05T17:10:09Z	--- base_model: dinalad0/L3-8B-Lunaris-cl language: - en library_name: transformers license: llama3 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/dinalad0/L3-8B-Lunaris-cl <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#L3-8B-Lunaris-cl-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mekala-2402/Model0-Q4_K_M-GGUF	mekala-2402	2025-09-05T20:12:53Z	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:mekala-2402/Model0", "base_model:quantized:mekala-2402/Model0", "endpoints_compatible", "region:us" ]	null	2025-09-05T20:12:40Z	--- base_model: mekala-2402/Model0 tags: - llama-cpp - gguf-my-repo --- # mekala-2402/Model0-Q4_K_M-GGUF This model was converted to GGUF format from [`mekala-2402/Model0`](https://huggingface.co/mekala-2402/Model0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mekala-2402/Model0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -c 2048 ```
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-IQ1_BN-SPECIAL_SPLIT	Thireus	2025-09-05T20:11:19Z	5	null	[ "gguf", "arxiv:2505.23786", "license:mit", "region:us" ]	null	2025-07-24T06:32:00Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
cwayneconnor/blockassist-bc-mute_loud_lynx_1757102815	cwayneconnor	2025-09-05T20:11:01Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute loud lynx", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:08:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute loud lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
popouy/blockassist-bc-mute_whistling_hamster_1757103017	popouy	2025-09-05T20:10:40Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute whistling hamster", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:10:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute whistling hamster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1757101473	koloni	2025-09-05T20:10:29Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:10:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1757101312	kojeklollipop	2025-09-05T20:10:26Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:10:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-IQ2_KT-SPECIAL_SPLIT	Thireus	2025-09-05T20:08:54Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-24T22:37:42Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
canoplos112/blockassist-bc-yapping_sleek_squirrel_1757102769	canoplos112	2025-09-05T20:08:26Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:06:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
popouy/blockassist-bc-prowling_aquatic_baboon_1757102781	popouy	2025-09-05T20:06:43Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prowling aquatic baboon", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:06:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prowling aquatic baboon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q8_K_R8-SPECIAL_SPLIT	Thireus	2025-09-05T20:06:32Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:10:06Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
bah63843/blockassist-bc-plump_fast_antelope_1757102689	bah63843	2025-09-05T20:05:49Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:05:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q8_0_R8-SPECIAL_SPLIT	Thireus	2025-09-05T20:05:22Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:09:59Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
rettertop/blockassist-bc-miniature_enormous_dolphin_1757102661	rettertop	2025-09-05T20:04:57Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "miniature enormous dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:04:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - miniature enormous dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
18-Sophie-Rain-Spider-Man-Vi-deo-Tutori-al/Sophie.Rain.Spiderman.Video.Tutorial	18-Sophie-Rain-Spider-Man-Vi-deo-Tutori-al	2025-09-05T20:03:17Z	0	null	[ "region:us" ]	null	2025-09-05T19:58:31Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q6_0_R4-SPECIAL_SPLIT	Thireus	2025-09-05T20:01:43Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:09:32Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
bah63843/blockassist-bc-plump_fast_antelope_1757102374	bah63843	2025-09-05T20:00:33Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:00:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q6_0-SPECIAL_SPLIT	Thireus	2025-09-05T20:00:29Z	1	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:09:25Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
popouy/blockassist-bc-amphibious_slender_dingo_1757102402	popouy	2025-09-05T20:00:29Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious slender dingo", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T20:00:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious slender dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ghosthash1/AlphaNeural	ghosthash1	2025-09-05T19:59:57Z	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Qwen/Qwen-Image", "base_model:adapter:Qwen/Qwen-Image", "license:apache-2.0", "region:us" ]	text-to-image	2025-09-05T19:59:55Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/Unknown-1.jpeg text: '-' base_model: Qwen/Qwen-Image instance_prompt: null license: apache-2.0 --- # AlphaNeural <Gallery /> ## Download model [Download](/ghosthash1/AlphaNeural/tree/main) them in the Files & versions tab.
moyixiao/Qwen3-0.6B-bnpo-f162-100	moyixiao	2025-09-05T19:59:37Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-05T19:59:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q5_K_R4-SPECIAL_SPLIT	Thireus	2025-09-05T19:59:16Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:09:18Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
ultratopaz/1258929	ultratopaz	2025-09-05T19:58:47Z	0	null	[ "region:us" ]	null	2025-09-05T19:58:48Z	[View on Civ Archive](https://civarchive.com/models/1201559?modelVersionId=1352969)
crystalline7/1296403	crystalline7	2025-09-05T19:58:36Z	0	null	[ "region:us" ]	null	2025-09-05T19:58:36Z	[View on Civ Archive](https://civarchive.com/models/1235078?modelVersionId=1391868)
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q5_1-SPECIAL_SPLIT	Thireus	2025-09-05T19:58:02Z	1	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:09:12Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
beyoru/Qwen3-M2N2-Dynamic	beyoru	2025-09-05T19:57:45Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "agent", "math", "code", "assistant", "merge", "conversational", "base_model:unsloth/Qwen3-0.6B", "base_model:finetune:unsloth/Qwen3-0.6B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-05T06:33:42Z	--- library_name: transformers tags: - agent - math - code - assistant - merge base_model: - unsloth/Qwen3-0.6B --- # Model: Qwen3-M2N2-Dynamic ## Model Overview Qwen3-M2N2-Dynamic is an extension of `beyoru/Qwen3-M2N2`, designed to enhance evolutionary computation with dynamic population management and parent selection heuristics. The development occurs in two stages: Stage 1: Qwen3-M2N2 * Implements competition sharing (`evaluate_population` with share allocation). * Supports M2N2 merge (SLERP + split). * Boundary remains static (single point based on `W_s`). * Parent selection relies on top-2 fitness only; no attraction heuristic. Stage 2: Qwen3-M2N2-Dynamic * Introduces dynamic split for more flexible boundary handling. * Implements attraction-based parent selection. * Enhances population diversity. --- ## Intended Use * Research in evolutionary algorithms and population-based optimization. * Comparison and benchmarking against baseline models like `Qwen3-0.6B`. * Use in both single-turn and multi-turn function evaluation scenarios. --- ## Evaluation ### Agent Performance non reasoning for both model The table summarizes model performance across different test categories: \| Test Category \| Qwen3-M2N2-Dynamic \| Qwen3-0.6B \| P1 \| P2 \| \| ------------------------------------ \| ------------------ \| ---------- \| ---------- \| ----------- \| \| Normal Single Turn Single Function \| 0.26 \| 0.19 \| 0.24 \| 0.30 \| \| Normal Single Turn Parallel Function \| 0.03 \| 0.09 \| 0.03 \| 0.04 \| \| Normal Multi Turn User Adjust \| 0.08 \| 0.00 \| 0.02 \| 0.12 \| \| Normal Multi Turn User Switch \| 0.10 \| 0.02 \| 0.08 \| 0.12 \| \| Normal Similar API \| 0.34 \| 0.40 \| 0.34 \| 0.42 \| \| Normal Preference \| 0.14 \| 0.10 \| 0.12 \| 0.14 \| \| Normal Atom Bool \| 0.46 \| 0.32 \| 0.46 \| 0.48 \| \| Normal Atom Enum \| 0.42 \| 0.26 \| 0.42 \| 0.50 \| \| Normal Atom Number \| 0.46 \| 0.40 \| 0.40 \| 0.46 \| \| Normal Atom List \| 0.48 \| 0.28 \| 0.44 \| 0.54 \| \| Normal Atom Object Deep \| 0.12 \| 0.12 \| 0.10 \| 0.14 \| \| Normal Atom Object Short \| 0.28 \| 0.28 \| 0.28 \| 0.38 \| \| Overall \| 0.27 \| 0.20 \| 0.24 \| 0.33 \| --- on-going test more ## Limitations * Static boundary selection remains in Stage 1. * Stage 2 introduces dynamic split, but performance varies by task category. * Attraction heuristic may bias parent selection toward high-fitness individuals, potentially reducing exploration. * This is only a test version, so only a small set to used for training. ---
amethyst9/1275521	amethyst9	2025-09-05T19:57:24Z	0	null	[ "region:us" ]	null	2025-09-05T19:57:24Z	[View on Civ Archive](https://civarchive.com/models/1215999?modelVersionId=1369769)
ultratopaz/1219846	ultratopaz	2025-09-05T19:57:12Z	0	null	[ "region:us" ]	null	2025-09-05T19:57:13Z	[View on Civ Archive](https://civarchive.com/models/1168489?modelVersionId=1314603)
seraphimzzzz/1318223	seraphimzzzz	2025-09-05T19:57:00Z	0	null	[ "region:us" ]	null	2025-09-05T19:57:01Z	[View on Civ Archive](https://civarchive.com/models/1255580?modelVersionId=1415579)
amethyst9/1330428	amethyst9	2025-09-05T19:56:48Z	0	null	[ "region:us" ]	null	2025-09-05T19:56:49Z	[View on Civ Archive](https://civarchive.com/models/1266054?modelVersionId=1427903)
ultratopaz/1285684	ultratopaz	2025-09-05T19:56:36Z	0	null	[ "region:us" ]	null	2025-09-05T19:56:37Z	[View on Civ Archive](https://civarchive.com/models/1226063?modelVersionId=1381426)
popouy/blockassist-bc-bellowing_mammalian_camel_1757102167	popouy	2025-09-05T19:56:30Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing mammalian camel", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T19:56:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing mammalian camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gopterwegop/blockassist-bc-beaked_exotic_spider_1757102160	gopterwegop	2025-09-05T19:56:21Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked exotic spider", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T19:56:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked exotic spider --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kafa22/blockassist-bc-regal_leggy_hummingbird_1757102137	kafa22	2025-09-05T19:56:18Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal leggy hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T19:56:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal leggy hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultratopaz/1252582	ultratopaz	2025-09-05T19:56:12Z	0	null	[ "region:us" ]	null	2025-09-05T19:56:13Z	[View on Civ Archive](https://civarchive.com/models/1145757?modelVersionId=1349097)
qgallouedec/Qwen3-8B-SFT-20250905191102	qgallouedec	2025-09-05T19:56:01Z	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "hf_jobs", "trl", "sft", "dataset:trl-lib/Capybara", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "endpoints_compatible", "region:us" ]	null	2025-09-05T19:11:55Z	--- base_model: Qwen/Qwen3-8B datasets: trl-lib/Capybara library_name: transformers model_name: Qwen3-8B-SFT-20250905191102 tags: - generated_from_trainer - hf_jobs - trl - sft licence: license --- # Model Card for Qwen3-8B-SFT-20250905191102 This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="qgallouedec/Qwen3-8B-SFT-20250905191102", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0.dev0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu128 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ultratopaz/1259974	ultratopaz	2025-09-05T19:55:36Z	0	null	[ "region:us" ]	null	2025-09-05T19:55:36Z	[View on Civ Archive](https://civarchive.com/models/1202079?modelVersionId=1353577)
hellonmn/llama3-code-generator	hellonmn	2025-09-05T19:55:15Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-05T19:54:42Z	--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hellonmn - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
crystalline7/1304358	crystalline7	2025-09-05T19:55:12Z	0	null	[ "region:us" ]	null	2025-09-05T19:55:12Z	[View on Civ Archive](https://civarchive.com/models/1241157?modelVersionId=1398842)
golopper/blockassist-bc-lithe_hulking_wasp_1757102035	golopper	2025-09-05T19:54:13Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lithe hulking wasp", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T19:53:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lithe hulking wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/1244045	amethyst9	2025-09-05T19:54:02Z	0	null	[ "region:us" ]	null	2025-09-05T19:54:02Z	[View on Civ Archive](https://civarchive.com/models/1189552?modelVersionId=1339185)
crystalline7/1229895	crystalline7	2025-09-05T19:53:49Z	0	null	[ "region:us" ]	null	2025-09-05T19:53:49Z	[View on Civ Archive](https://civarchive.com/models/1176514?modelVersionId=1323817)
nesall/nomic-embed-text-v1.5-Q6_K-GGUF	nesall	2025-09-05T19:53:48Z	0	sentence-transformers	[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js", "llama-cpp", "gguf-my-repo", "en", "base_model:nomic-ai/nomic-embed-text-v1.5", "base_model:quantized:nomic-ai/nomic-embed-text-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-05T19:53:44Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - mteb - transformers - transformers.js - llama-cpp - gguf-my-repo license: apache-2.0 language: - en base_model: nomic-ai/nomic-embed-text-v1.5 model-index: - name: epoch_0_model results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.20895522388058 - type: ap value: 38.57605549557802 - type: f1 value: 69.35586565857854 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.8144 - type: ap value: 88.65222882032363 - type: f1 value: 91.80426301643274 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.162000000000006 - type: f1 value: 46.59329642263158 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 24.253 - type: map_at_10 value: 38.962 - type: map_at_100 value: 40.081 - type: map_at_1000 value: 40.089000000000006 - type: map_at_3 value: 33.499 - type: map_at_5 value: 36.351 - type: mrr_at_1 value: 24.609 - type: mrr_at_10 value: 39.099000000000004 - type: mrr_at_100 value: 40.211000000000006 - type: mrr_at_1000 value: 40.219 - type: mrr_at_3 value: 33.677 - type: mrr_at_5 value: 36.469 - type: ndcg_at_1 value: 24.253 - type: ndcg_at_10 value: 48.010999999999996 - type: ndcg_at_100 value: 52.756 - type: ndcg_at_1000 value: 52.964999999999996 - type: ndcg_at_3 value: 36.564 - type: ndcg_at_5 value: 41.711999999999996 - type: precision_at_1 value: 24.253 - type: precision_at_10 value: 7.738 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.149000000000001 - type: precision_at_5 value: 11.593 - type: recall_at_1 value: 24.253 - type: recall_at_10 value: 77.383 - type: recall_at_100 value: 98.009 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 45.448 - type: recall_at_5 value: 57.965999999999994 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.69069567851087 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.35185490976283 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.71274951450321 - type: mrr value: 76.06032625423207 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.73980520022269 - type: cos_sim_spearman value: 84.24649792685918 - type: euclidean_pearson value: 85.85197641158186 - type: euclidean_spearman value: 84.24649792685918 - type: manhattan_pearson value: 86.26809552711346 - type: manhattan_spearman value: 84.56397504030865 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.25324675324674 - type: f1 value: 84.17872280892557 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.770253446400886 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.94307095497281 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.164 - type: map_at_10 value: 42.641 - type: map_at_100 value: 43.947 - type: map_at_1000 value: 44.074999999999996 - type: map_at_3 value: 39.592 - type: map_at_5 value: 41.204 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 48.625 - type: mrr_at_100 value: 49.368 - type: mrr_at_1000 value: 49.413000000000004 - type: mrr_at_3 value: 46.400000000000006 - type: mrr_at_5 value: 47.68 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 48.564 - type: ndcg_at_100 value: 53.507000000000005 - type: ndcg_at_1000 value: 55.635999999999996 - type: ndcg_at_3 value: 44.471 - type: ndcg_at_5 value: 46.137 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 8.856 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 14.649000000000001 - type: recall_at_1 value: 32.164 - type: recall_at_10 value: 59.609 - type: recall_at_100 value: 80.521 - type: recall_at_1000 value: 94.245 - type: recall_at_3 value: 46.521 - type: recall_at_5 value: 52.083999999999996 - type: map_at_1 value: 31.526 - type: map_at_10 value: 41.581 - type: map_at_100 value: 42.815999999999995 - type: map_at_1000 value: 42.936 - type: map_at_3 value: 38.605000000000004 - type: map_at_5 value: 40.351 - type: mrr_at_1 value: 39.489999999999995 - type: mrr_at_10 value: 47.829 - type: mrr_at_100 value: 48.512 - type: mrr_at_1000 value: 48.552 - type: mrr_at_3 value: 45.754 - type: mrr_at_5 value: 46.986 - type: ndcg_at_1 value: 39.489999999999995 - type: ndcg_at_10 value: 47.269 - type: ndcg_at_100 value: 51.564 - type: ndcg_at_1000 value: 53.53099999999999 - type: ndcg_at_3 value: 43.301 - type: ndcg_at_5 value: 45.239000000000004 - type: precision_at_1 value: 39.489999999999995 - type: precision_at_10 value: 8.93 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 20.892 - type: precision_at_5 value: 14.865999999999998 - type: recall_at_1 value: 31.526 - type: recall_at_10 value: 56.76 - type: recall_at_100 value: 75.029 - type: recall_at_1000 value: 87.491 - type: recall_at_3 value: 44.786 - type: recall_at_5 value: 50.254 - type: map_at_1 value: 40.987 - type: map_at_10 value: 52.827 - type: map_at_100 value: 53.751000000000005 - type: map_at_1000 value: 53.81 - type: map_at_3 value: 49.844 - type: map_at_5 value: 51.473 - type: mrr_at_1 value: 46.833999999999996 - type: mrr_at_10 value: 56.389 - type: mrr_at_100 value: 57.003 - type: mrr_at_1000 value: 57.034 - type: mrr_at_3 value: 54.17999999999999 - type: mrr_at_5 value: 55.486999999999995 - type: ndcg_at_1 value: 46.833999999999996 - type: ndcg_at_10 value: 58.372 - type: ndcg_at_100 value: 62.068 - type: ndcg_at_1000 value: 63.288 - type: ndcg_at_3 value: 53.400000000000006 - type: ndcg_at_5 value: 55.766000000000005 - type: precision_at_1 value: 46.833999999999996 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.448 - type: precision_at_5 value: 15.862000000000002 - type: recall_at_1 value: 40.987 - type: recall_at_10 value: 71.146 - type: recall_at_100 value: 87.035 - type: recall_at_1000 value: 95.633 - type: recall_at_3 value: 58.025999999999996 - type: recall_at_5 value: 63.815999999999995 - type: map_at_1 value: 24.587 - type: map_at_10 value: 33.114 - type: map_at_100 value: 34.043 - type: map_at_1000 value: 34.123999999999995 - type: map_at_3 value: 30.45 - type: map_at_5 value: 31.813999999999997 - type: mrr_at_1 value: 26.554 - type: mrr_at_10 value: 35.148 - type: mrr_at_100 value: 35.926 - type: mrr_at_1000 value: 35.991 - type: mrr_at_3 value: 32.599000000000004 - type: mrr_at_5 value: 33.893 - type: ndcg_at_1 value: 26.554 - type: ndcg_at_10 value: 38.132 - type: ndcg_at_100 value: 42.78 - type: ndcg_at_1000 value: 44.919 - type: ndcg_at_3 value: 32.833 - type: ndcg_at_5 value: 35.168 - type: precision_at_1 value: 26.554 - type: precision_at_10 value: 5.921 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.861 - type: precision_at_5 value: 9.605 - type: recall_at_1 value: 24.587 - type: recall_at_10 value: 51.690000000000005 - type: recall_at_100 value: 73.428 - type: recall_at_1000 value: 89.551 - type: recall_at_3 value: 37.336999999999996 - type: recall_at_5 value: 43.047000000000004 - type: map_at_1 value: 16.715 - type: map_at_10 value: 24.251 - type: map_at_100 value: 25.326999999999998 - type: map_at_1000 value: 25.455 - type: map_at_3 value: 21.912000000000003 - type: map_at_5 value: 23.257 - type: mrr_at_1 value: 20.274 - type: mrr_at_10 value: 28.552 - type: mrr_at_100 value: 29.42 - type: mrr_at_1000 value: 29.497 - type: mrr_at_3 value: 26.14 - type: mrr_at_5 value: 27.502 - type: ndcg_at_1 value: 20.274 - type: ndcg_at_10 value: 29.088 - type: ndcg_at_100 value: 34.293 - type: ndcg_at_1000 value: 37.271 - type: ndcg_at_3 value: 24.708 - type: ndcg_at_5 value: 26.809 - type: precision_at_1 value: 20.274 - type: precision_at_10 value: 5.361 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.733 - type: precision_at_5 value: 8.556999999999999 - type: recall_at_1 value: 16.715 - type: recall_at_10 value: 39.587 - type: recall_at_100 value: 62.336000000000006 - type: recall_at_1000 value: 83.453 - type: recall_at_3 value: 27.839999999999996 - type: recall_at_5 value: 32.952999999999996 - type: map_at_1 value: 28.793000000000003 - type: map_at_10 value: 38.582 - type: map_at_100 value: 39.881 - type: map_at_1000 value: 39.987 - type: map_at_3 value: 35.851 - type: map_at_5 value: 37.289 - type: mrr_at_1 value: 34.455999999999996 - type: mrr_at_10 value: 43.909 - type: mrr_at_100 value: 44.74 - type: mrr_at_1000 value: 44.786 - type: mrr_at_3 value: 41.659 - type: mrr_at_5 value: 43.010999999999996 - type: ndcg_at_1 value: 34.455999999999996 - type: ndcg_at_10 value: 44.266 - type: ndcg_at_100 value: 49.639 - type: ndcg_at_1000 value: 51.644 - type: ndcg_at_3 value: 39.865 - type: ndcg_at_5 value: 41.887 - type: precision_at_1 value: 34.455999999999996 - type: precision_at_10 value: 7.843999999999999 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 18.831999999999997 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 28.793000000000003 - type: recall_at_10 value: 55.68300000000001 - type: recall_at_100 value: 77.99000000000001 - type: recall_at_1000 value: 91.183 - type: recall_at_3 value: 43.293 - type: recall_at_5 value: 48.618 - type: map_at_1 value: 25.907000000000004 - type: map_at_10 value: 35.519 - type: map_at_100 value: 36.806 - type: map_at_1000 value: 36.912 - type: map_at_3 value: 32.748 - type: map_at_5 value: 34.232 - type: mrr_at_1 value: 31.621 - type: mrr_at_10 value: 40.687 - type: mrr_at_100 value: 41.583 - type: mrr_at_1000 value: 41.638999999999996 - type: mrr_at_3 value: 38.527 - type: mrr_at_5 value: 39.612 - type: ndcg_at_1 value: 31.621 - type: ndcg_at_10 value: 41.003 - type: ndcg_at_100 value: 46.617999999999995 - type: ndcg_at_1000 value: 48.82 - type: ndcg_at_3 value: 36.542 - type: ndcg_at_5 value: 38.368 - type: precision_at_1 value: 31.621 - type: precision_at_10 value: 7.396999999999999 - type: precision_at_100 value: 1.191 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 17.39 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 25.907000000000004 - type: recall_at_10 value: 52.115 - type: recall_at_100 value: 76.238 - type: recall_at_1000 value: 91.218 - type: recall_at_3 value: 39.417 - type: recall_at_5 value: 44.435 - type: map_at_1 value: 25.732166666666668 - type: map_at_10 value: 34.51616666666667 - type: map_at_100 value: 35.67241666666666 - type: map_at_1000 value: 35.78675 - type: map_at_3 value: 31.953416666666662 - type: map_at_5 value: 33.333 - type: mrr_at_1 value: 30.300166666666673 - type: mrr_at_10 value: 38.6255 - type: mrr_at_100 value: 39.46183333333334 - type: mrr_at_1000 value: 39.519999999999996 - type: mrr_at_3 value: 36.41299999999999 - type: mrr_at_5 value: 37.6365 - type: ndcg_at_1 value: 30.300166666666673 - type: ndcg_at_10 value: 39.61466666666667 - type: ndcg_at_100 value: 44.60808333333334 - type: ndcg_at_1000 value: 46.91708333333334 - type: ndcg_at_3 value: 35.26558333333333 - type: ndcg_at_5 value: 37.220000000000006 - type: precision_at_1 value: 30.300166666666673 - type: precision_at_10 value: 6.837416666666667 - type: precision_at_100 value: 1.10425 - type: precision_at_1000 value: 0.14875 - type: precision_at_3 value: 16.13716666666667 - type: precision_at_5 value: 11.2815 - type: recall_at_1 value: 25.732166666666668 - type: recall_at_10 value: 50.578916666666665 - type: recall_at_100 value: 72.42183333333334 - type: recall_at_1000 value: 88.48766666666667 - type: recall_at_3 value: 38.41325 - type: recall_at_5 value: 43.515750000000004 - type: map_at_1 value: 23.951 - type: map_at_10 value: 30.974 - type: map_at_100 value: 31.804 - type: map_at_1000 value: 31.900000000000002 - type: map_at_3 value: 28.762 - type: map_at_5 value: 29.94 - type: mrr_at_1 value: 26.534000000000002 - type: mrr_at_10 value: 33.553 - type: mrr_at_100 value: 34.297 - type: mrr_at_1000 value: 34.36 - type: mrr_at_3 value: 31.391000000000002 - type: mrr_at_5 value: 32.525999999999996 - type: ndcg_at_1 value: 26.534000000000002 - type: ndcg_at_10 value: 35.112 - type: ndcg_at_100 value: 39.28 - type: ndcg_at_1000 value: 41.723 - type: ndcg_at_3 value: 30.902 - type: ndcg_at_5 value: 32.759 - type: precision_at_1 value: 26.534000000000002 - type: precision_at_10 value: 5.445 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.951 - type: recall_at_10 value: 45.24 - type: recall_at_100 value: 64.12299999999999 - type: recall_at_1000 value: 82.28999999999999 - type: recall_at_3 value: 33.806000000000004 - type: recall_at_5 value: 38.277 - type: map_at_1 value: 16.829 - type: map_at_10 value: 23.684 - type: map_at_100 value: 24.683 - type: map_at_1000 value: 24.81 - type: map_at_3 value: 21.554000000000002 - type: map_at_5 value: 22.768 - type: mrr_at_1 value: 20.096 - type: mrr_at_10 value: 27.230999999999998 - type: mrr_at_100 value: 28.083999999999996 - type: mrr_at_1000 value: 28.166000000000004 - type: mrr_at_3 value: 25.212 - type: mrr_at_5 value: 26.32 - type: ndcg_at_1 value: 20.096 - type: ndcg_at_10 value: 27.989000000000004 - type: ndcg_at_100 value: 32.847 - type: ndcg_at_1000 value: 35.896 - type: ndcg_at_3 value: 24.116 - type: ndcg_at_5 value: 25.964 - type: precision_at_1 value: 20.096 - type: precision_at_10 value: 5 - type: precision_at_100 value: 0.8750000000000001 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 11.207 - type: precision_at_5 value: 8.08 - type: recall_at_1 value: 16.829 - type: recall_at_10 value: 37.407000000000004 - type: recall_at_100 value: 59.101000000000006 - type: recall_at_1000 value: 81.024 - type: recall_at_3 value: 26.739 - type: recall_at_5 value: 31.524 - type: map_at_1 value: 24.138 - type: map_at_10 value: 32.275999999999996 - type: map_at_100 value: 33.416000000000004 - type: map_at_1000 value: 33.527 - type: map_at_3 value: 29.854000000000003 - type: map_at_5 value: 31.096 - type: mrr_at_1 value: 28.450999999999997 - type: mrr_at_10 value: 36.214 - type: mrr_at_100 value: 37.134 - type: mrr_at_1000 value: 37.198 - type: mrr_at_3 value: 34.001999999999995 - type: mrr_at_5 value: 35.187000000000005 - type: ndcg_at_1 value: 28.450999999999997 - type: ndcg_at_10 value: 37.166 - type: ndcg_at_100 value: 42.454 - type: ndcg_at_1000 value: 44.976 - type: ndcg_at_3 value: 32.796 - type: ndcg_at_5 value: 34.631 - type: precision_at_1 value: 28.450999999999997 - type: precision_at_10 value: 6.241 - type: precision_at_100 value: 0.9950000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.801 - type: precision_at_5 value: 10.280000000000001 - type: recall_at_1 value: 24.138 - type: recall_at_10 value: 48.111 - type: recall_at_100 value: 71.245 - type: recall_at_1000 value: 88.986 - type: recall_at_3 value: 36.119 - type: recall_at_5 value: 40.846 - type: map_at_1 value: 23.244 - type: map_at_10 value: 31.227 - type: map_at_100 value: 33.007 - type: map_at_1000 value: 33.223 - type: map_at_3 value: 28.924 - type: map_at_5 value: 30.017 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 35.524 - type: mrr_at_100 value: 36.699 - type: mrr_at_1000 value: 36.759 - type: mrr_at_3 value: 33.366 - type: mrr_at_5 value: 34.552 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 36.381 - type: ndcg_at_100 value: 43.062 - type: ndcg_at_1000 value: 45.656 - type: ndcg_at_3 value: 32.501999999999995 - type: ndcg_at_5 value: 34.105999999999995 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 6.798 - type: precision_at_100 value: 1.492 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 15.152 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.244 - type: recall_at_10 value: 45.979 - type: recall_at_100 value: 74.822 - type: recall_at_1000 value: 91.078 - type: recall_at_3 value: 34.925 - type: recall_at_5 value: 39.126 - type: map_at_1 value: 19.945 - type: map_at_10 value: 27.517999999999997 - type: map_at_100 value: 28.588 - type: map_at_1000 value: 28.682000000000002 - type: map_at_3 value: 25.345000000000002 - type: map_at_5 value: 26.555 - type: mrr_at_1 value: 21.996 - type: mrr_at_10 value: 29.845 - type: mrr_at_100 value: 30.775999999999996 - type: mrr_at_1000 value: 30.845 - type: mrr_at_3 value: 27.726 - type: mrr_at_5 value: 28.882 - type: ndcg_at_1 value: 21.996 - type: ndcg_at_10 value: 32.034 - type: ndcg_at_100 value: 37.185 - type: ndcg_at_1000 value: 39.645 - type: ndcg_at_3 value: 27.750999999999998 - type: ndcg_at_5 value: 29.805999999999997 - type: precision_at_1 value: 21.996 - type: precision_at_10 value: 5.065 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.392 - type: recall_at_1 value: 19.945 - type: recall_at_10 value: 43.62 - type: recall_at_100 value: 67.194 - type: recall_at_1000 value: 85.7 - type: recall_at_3 value: 32.15 - type: recall_at_5 value: 37.208999999999996 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 18.279 - type: map_at_10 value: 31.052999999999997 - type: map_at_100 value: 33.125 - type: map_at_1000 value: 33.306000000000004 - type: map_at_3 value: 26.208 - type: map_at_5 value: 28.857 - type: mrr_at_1 value: 42.671 - type: mrr_at_10 value: 54.557 - type: mrr_at_100 value: 55.142 - type: mrr_at_1000 value: 55.169000000000004 - type: mrr_at_3 value: 51.488 - type: mrr_at_5 value: 53.439 - type: ndcg_at_1 value: 42.671 - type: ndcg_at_10 value: 41.276 - type: ndcg_at_100 value: 48.376000000000005 - type: ndcg_at_1000 value: 51.318 - type: ndcg_at_3 value: 35.068 - type: ndcg_at_5 value: 37.242 - type: precision_at_1 value: 42.671 - type: precision_at_10 value: 12.638 - type: precision_at_100 value: 2.045 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 26.08 - type: precision_at_5 value: 19.805 - type: recall_at_1 value: 18.279 - type: recall_at_10 value: 46.946 - type: recall_at_100 value: 70.97200000000001 - type: recall_at_1000 value: 87.107 - type: recall_at_3 value: 31.147999999999996 - type: recall_at_5 value: 38.099 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.573 - type: map_at_10 value: 19.747 - type: map_at_100 value: 28.205000000000002 - type: map_at_1000 value: 29.831000000000003 - type: map_at_3 value: 14.109 - type: map_at_5 value: 16.448999999999998 - type: mrr_at_1 value: 71 - type: mrr_at_10 value: 77.68599999999999 - type: mrr_at_100 value: 77.995 - type: mrr_at_1000 value: 78.00200000000001 - type: mrr_at_3 value: 76.292 - type: mrr_at_5 value: 77.029 - type: ndcg_at_1 value: 59.12500000000001 - type: ndcg_at_10 value: 43.9 - type: ndcg_at_100 value: 47.863 - type: ndcg_at_1000 value: 54.848 - type: ndcg_at_3 value: 49.803999999999995 - type: ndcg_at_5 value: 46.317 - type: precision_at_1 value: 71 - type: precision_at_10 value: 34.4 - type: precision_at_100 value: 11.063 - type: precision_at_1000 value: 1.989 - type: precision_at_3 value: 52.333 - type: precision_at_5 value: 43.7 - type: recall_at_1 value: 8.573 - type: recall_at_10 value: 25.615 - type: recall_at_100 value: 53.385000000000005 - type: recall_at_1000 value: 75.46000000000001 - type: recall_at_3 value: 15.429 - type: recall_at_5 value: 19.357 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.989999999999995 - type: f1 value: 42.776314451497555 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.13499999999999 - type: map_at_10 value: 82.825 - type: map_at_100 value: 83.096 - type: map_at_1000 value: 83.111 - type: map_at_3 value: 81.748 - type: map_at_5 value: 82.446 - type: mrr_at_1 value: 79.553 - type: mrr_at_10 value: 86.654 - type: mrr_at_100 value: 86.774 - type: mrr_at_1000 value: 86.778 - type: mrr_at_3 value: 85.981 - type: mrr_at_5 value: 86.462 - type: ndcg_at_1 value: 79.553 - type: ndcg_at_10 value: 86.345 - type: ndcg_at_100 value: 87.32 - type: ndcg_at_1000 value: 87.58200000000001 - type: ndcg_at_3 value: 84.719 - type: ndcg_at_5 value: 85.677 - type: precision_at_1 value: 79.553 - type: precision_at_10 value: 10.402000000000001 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.413 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 74.13499999999999 - type: recall_at_10 value: 93.215 - type: recall_at_100 value: 97.083 - type: recall_at_1000 value: 98.732 - type: recall_at_3 value: 88.79 - type: recall_at_5 value: 91.259 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 18.298000000000002 - type: map_at_10 value: 29.901 - type: map_at_100 value: 31.528 - type: map_at_1000 value: 31.713 - type: map_at_3 value: 25.740000000000002 - type: map_at_5 value: 28.227999999999998 - type: mrr_at_1 value: 36.728 - type: mrr_at_10 value: 45.401 - type: mrr_at_100 value: 46.27 - type: mrr_at_1000 value: 46.315 - type: mrr_at_3 value: 42.978 - type: mrr_at_5 value: 44.29 - type: ndcg_at_1 value: 36.728 - type: ndcg_at_10 value: 37.456 - type: ndcg_at_100 value: 43.832 - type: ndcg_at_1000 value: 47 - type: ndcg_at_3 value: 33.694 - type: ndcg_at_5 value: 35.085 - type: precision_at_1 value: 36.728 - type: precision_at_10 value: 10.386 - type: precision_at_100 value: 1.701 - type: precision_at_1000 value: 0.22599999999999998 - type: precision_at_3 value: 22.479 - type: precision_at_5 value: 16.605 - type: recall_at_1 value: 18.298000000000002 - type: recall_at_10 value: 44.369 - type: recall_at_100 value: 68.098 - type: recall_at_1000 value: 87.21900000000001 - type: recall_at_3 value: 30.215999999999998 - type: recall_at_5 value: 36.861 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.568 - type: map_at_10 value: 65.061 - type: map_at_100 value: 65.896 - type: map_at_1000 value: 65.95100000000001 - type: map_at_3 value: 61.831 - type: map_at_5 value: 63.849000000000004 - type: mrr_at_1 value: 79.136 - type: mrr_at_10 value: 84.58200000000001 - type: mrr_at_100 value: 84.765 - type: mrr_at_1000 value: 84.772 - type: mrr_at_3 value: 83.684 - type: mrr_at_5 value: 84.223 - type: ndcg_at_1 value: 79.136 - type: ndcg_at_10 value: 72.622 - type: ndcg_at_100 value: 75.539 - type: ndcg_at_1000 value: 76.613 - type: ndcg_at_3 value: 68.065 - type: ndcg_at_5 value: 70.58 - type: precision_at_1 value: 79.136 - type: precision_at_10 value: 15.215 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 44.011 - type: precision_at_5 value: 28.388999999999996 - type: recall_at_1 value: 39.568 - type: recall_at_10 value: 76.077 - type: recall_at_100 value: 87.481 - type: recall_at_1000 value: 94.56400000000001 - type: recall_at_3 value: 66.01599999999999 - type: recall_at_5 value: 70.97200000000001 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.312 - type: ap value: 80.36296867333715 - type: f1 value: 85.26613311552218 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.363999999999997 - type: map_at_10 value: 35.711999999999996 - type: map_at_100 value: 36.876999999999995 - type: map_at_1000 value: 36.923 - type: map_at_3 value: 32.034 - type: map_at_5 value: 34.159 - type: mrr_at_1 value: 24.04 - type: mrr_at_10 value: 36.345 - type: mrr_at_100 value: 37.441 - type: mrr_at_1000 value: 37.480000000000004 - type: mrr_at_3 value: 32.713 - type: mrr_at_5 value: 34.824 - type: ndcg_at_1 value: 24.026 - type: ndcg_at_10 value: 42.531 - type: ndcg_at_100 value: 48.081 - type: ndcg_at_1000 value: 49.213 - type: ndcg_at_3 value: 35.044 - type: ndcg_at_5 value: 38.834 - type: precision_at_1 value: 24.026 - type: precision_at_10 value: 6.622999999999999 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.909 - type: precision_at_5 value: 10.871 - type: recall_at_1 value: 23.363999999999997 - type: recall_at_10 value: 63.426 - type: recall_at_100 value: 88.96300000000001 - type: recall_at_1000 value: 97.637 - type: recall_at_3 value: 43.095 - type: recall_at_5 value: 52.178000000000004 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.0095759233926 - type: f1 value: 92.78387794667408 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.0296397628819 - type: f1 value: 58.45699589820874 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.45662407531944 - type: f1 value: 71.42364781421813 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.07800941492937 - type: f1 value: 77.22799045640845 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.531234379250606 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.941490381193802 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.3115090856725 - type: mrr value: 31.290667638675757 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.465 - type: map_at_10 value: 13.03 - type: map_at_100 value: 16.057 - type: map_at_1000 value: 17.49 - type: map_at_3 value: 9.553 - type: map_at_5 value: 11.204 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.269 - type: mrr_at_100 value: 53.72 - type: mrr_at_1000 value: 53.761 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 52.461 - type: ndcg_at_1 value: 42.26 - type: ndcg_at_10 value: 34.673 - type: ndcg_at_100 value: 30.759999999999998 - type: ndcg_at_1000 value: 39.728 - type: ndcg_at_3 value: 40.349000000000004 - type: ndcg_at_5 value: 37.915 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.789 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.596000000000004 - type: precision_at_5 value: 33.251 - type: recall_at_1 value: 5.465 - type: recall_at_10 value: 17.148 - type: recall_at_100 value: 29.768 - type: recall_at_1000 value: 62.239 - type: recall_at_3 value: 10.577 - type: recall_at_5 value: 13.315 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 37.008 - type: map_at_10 value: 52.467 - type: map_at_100 value: 53.342999999999996 - type: map_at_1000 value: 53.366 - type: map_at_3 value: 48.412 - type: map_at_5 value: 50.875 - type: mrr_at_1 value: 41.541 - type: mrr_at_10 value: 54.967 - type: mrr_at_100 value: 55.611 - type: mrr_at_1000 value: 55.627 - type: mrr_at_3 value: 51.824999999999996 - type: mrr_at_5 value: 53.763000000000005 - type: ndcg_at_1 value: 41.541 - type: ndcg_at_10 value: 59.724999999999994 - type: ndcg_at_100 value: 63.38700000000001 - type: ndcg_at_1000 value: 63.883 - type: ndcg_at_3 value: 52.331 - type: ndcg_at_5 value: 56.327000000000005 - type: precision_at_1 value: 41.541 - type: precision_at_10 value: 9.447 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.262 - type: precision_at_5 value: 16.314999999999998 - type: recall_at_1 value: 37.008 - type: recall_at_10 value: 79.145 - type: recall_at_100 value: 94.986 - type: recall_at_1000 value: 98.607 - type: recall_at_3 value: 60.277 - type: recall_at_5 value: 69.407 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.402 - type: map_at_10 value: 84.181 - type: map_at_100 value: 84.796 - type: map_at_1000 value: 84.81400000000001 - type: map_at_3 value: 81.209 - type: map_at_5 value: 83.085 - type: mrr_at_1 value: 81.02000000000001 - type: mrr_at_10 value: 87.263 - type: mrr_at_100 value: 87.36 - type: mrr_at_1000 value: 87.36 - type: mrr_at_3 value: 86.235 - type: mrr_at_5 value: 86.945 - type: ndcg_at_1 value: 81.01 - type: ndcg_at_10 value: 87.99900000000001 - type: ndcg_at_100 value: 89.217 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 85.053 - type: ndcg_at_5 value: 86.703 - type: precision_at_1 value: 81.01 - type: precision_at_10 value: 13.336 - type: precision_at_100 value: 1.52 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 24.44 - type: recall_at_1 value: 70.402 - type: recall_at_10 value: 95.214 - type: recall_at_100 value: 99.438 - type: recall_at_1000 value: 99.928 - type: recall_at_3 value: 86.75699999999999 - type: recall_at_5 value: 91.44099999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.51721502758904 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 61.054808572333016 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.578 - type: map_at_10 value: 11.036999999999999 - type: map_at_100 value: 12.879999999999999 - type: map_at_1000 value: 13.150999999999998 - type: map_at_3 value: 8.133 - type: map_at_5 value: 9.559 - type: mrr_at_1 value: 22.6 - type: mrr_at_10 value: 32.68 - type: mrr_at_100 value: 33.789 - type: mrr_at_1000 value: 33.854 - type: mrr_at_3 value: 29.7 - type: mrr_at_5 value: 31.480000000000004 - type: ndcg_at_1 value: 22.6 - type: ndcg_at_10 value: 18.616 - type: ndcg_at_100 value: 25.883 - type: ndcg_at_1000 value: 30.944 - type: ndcg_at_3 value: 18.136 - type: ndcg_at_5 value: 15.625 - type: precision_at_1 value: 22.6 - type: precision_at_10 value: 9.48 - type: precision_at_100 value: 1.991 - type: precision_at_1000 value: 0.321 - type: precision_at_3 value: 16.8 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.578 - type: recall_at_10 value: 19.213 - type: recall_at_100 value: 40.397 - type: recall_at_1000 value: 65.2 - type: recall_at_3 value: 10.208 - type: recall_at_5 value: 13.718 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.44288351714071 - type: cos_sim_spearman value: 79.37995604564952 - type: euclidean_pearson value: 81.1078874670718 - type: euclidean_spearman value: 79.37995905980499 - type: manhattan_pearson value: 81.03697527288986 - type: manhattan_spearman value: 79.33490235296236 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.95557650436523 - type: cos_sim_spearman value: 78.5190672399868 - type: euclidean_pearson value: 81.58064025904707 - type: euclidean_spearman value: 78.5190672399868 - type: manhattan_pearson value: 81.52857930619889 - type: manhattan_spearman value: 78.50421361308034 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.79128416228737 - type: cos_sim_spearman value: 86.05402451477147 - type: euclidean_pearson value: 85.46280267054289 - type: euclidean_spearman value: 86.05402451477147 - type: manhattan_pearson value: 85.46278563858236 - type: manhattan_spearman value: 86.08079590861004 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - 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type: manhattan_pearson value: 65.88143604989976 - type: manhattan_spearman value: 65.17547297222434 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.22876368947644 - type: cos_sim_spearman value: 85.46935577445318 - type: euclidean_pearson value: 85.32830231392005 - type: euclidean_spearman value: 85.46935577445318 - type: manhattan_pearson value: 85.30353211758495 - type: manhattan_spearman value: 85.42821085956945 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 80.60986667767133 - type: mrr value: 94.29432314236236 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 54.528 - type: map_at_10 value: 65.187 - type: map_at_100 value: 65.62599999999999 - type: map_at_1000 value: 65.657 - type: map_at_3 value: 62.352 - type: map_at_5 value: 64.025 - type: mrr_at_1 value: 57.333 - type: mrr_at_10 value: 66.577 - type: mrr_at_100 value: 66.88 - type: mrr_at_1000 value: 66.908 - type: mrr_at_3 value: 64.556 - type: mrr_at_5 value: 65.739 - type: ndcg_at_1 value: 57.333 - type: ndcg_at_10 value: 70.275 - type: ndcg_at_100 value: 72.136 - type: ndcg_at_1000 value: 72.963 - type: ndcg_at_3 value: 65.414 - type: ndcg_at_5 value: 67.831 - type: precision_at_1 value: 57.333 - type: precision_at_10 value: 9.5 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.778000000000002 - type: precision_at_5 value: 17.2 - type: recall_at_1 value: 54.528 - type: recall_at_10 value: 84.356 - type: recall_at_100 value: 92.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 71.283 - type: recall_at_5 value: 77.14999999999999 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.74158415841585 - type: cos_sim_ap value: 92.90048959850317 - type: cos_sim_f1 value: 86.35650810245687 - type: cos_sim_precision value: 90.4709748083242 - type: cos_sim_recall value: 82.6 - type: dot_accuracy value: 99.74158415841585 - type: dot_ap value: 92.90048959850317 - type: dot_f1 value: 86.35650810245687 - type: dot_precision value: 90.4709748083242 - type: dot_recall value: 82.6 - type: euclidean_accuracy value: 99.74158415841585 - type: euclidean_ap value: 92.90048959850317 - type: euclidean_f1 value: 86.35650810245687 - type: euclidean_precision value: 90.4709748083242 - type: euclidean_recall value: 82.6 - type: manhattan_accuracy value: 99.74158415841585 - type: manhattan_ap value: 92.87344692947894 - type: manhattan_f1 value: 86.38497652582159 - type: manhattan_precision value: 90.29443838604145 - type: manhattan_recall value: 82.8 - type: max_accuracy value: 99.74158415841585 - type: max_ap value: 92.90048959850317 - type: max_f1 value: 86.38497652582159 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 63.191648770424216 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.02944668730218 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.466386167525265 - type: mrr value: 51.19071492233257 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.198022505886435 - type: cos_sim_spearman value: 30.40170257939193 - type: dot_pearson value: 30.198015316402614 - type: dot_spearman value: 30.40170257939193 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.242 - type: map_at_10 value: 2.17 - type: map_at_100 value: 12.221 - type: map_at_1000 value: 28.63 - type: map_at_3 value: 0.728 - type: map_at_5 value: 1.185 - type: mrr_at_1 value: 94 - type: mrr_at_10 value: 97 - type: mrr_at_100 value: 97 - type: mrr_at_1000 value: 97 - type: mrr_at_3 value: 97 - type: mrr_at_5 value: 97 - type: ndcg_at_1 value: 89 - type: ndcg_at_10 value: 82.30499999999999 - type: ndcg_at_100 value: 61.839999999999996 - type: ndcg_at_1000 value: 53.381 - type: ndcg_at_3 value: 88.877 - type: ndcg_at_5 value: 86.05199999999999 - type: precision_at_1 value: 94 - type: precision_at_10 value: 87 - type: precision_at_100 value: 63.38 - type: precision_at_1000 value: 23.498 - type: precision_at_3 value: 94 - type: precision_at_5 value: 92 - type: recall_at_1 value: 0.242 - type: recall_at_10 value: 2.302 - type: recall_at_100 value: 14.979000000000001 - type: recall_at_1000 value: 49.638 - type: recall_at_3 value: 0.753 - type: recall_at_5 value: 1.226 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.006 - type: map_at_10 value: 11.805 - type: map_at_100 value: 18.146 - type: map_at_1000 value: 19.788 - type: map_at_3 value: 5.914 - type: map_at_5 value: 8.801 - type: mrr_at_1 value: 40.816 - type: mrr_at_10 value: 56.36600000000001 - type: mrr_at_100 value: 56.721999999999994 - type: mrr_at_1000 value: 56.721999999999994 - type: mrr_at_3 value: 52.041000000000004 - type: mrr_at_5 value: 54.796 - type: ndcg_at_1 value: 37.755 - type: ndcg_at_10 value: 29.863 - type: ndcg_at_100 value: 39.571 - type: ndcg_at_1000 value: 51.385999999999996 - type: ndcg_at_3 value: 32.578 - type: ndcg_at_5 value: 32.351 - type: precision_at_1 value: 40.816 - type: precision_at_10 value: 26.531 - type: precision_at_100 value: 7.796 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 32.653 - type: precision_at_5 value: 33.061 - type: recall_at_1 value: 3.006 - type: recall_at_10 value: 18.738 - type: recall_at_100 value: 48.058 - type: recall_at_1000 value: 83.41300000000001 - type: recall_at_3 value: 7.166 - type: recall_at_5 value: 12.102 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.4178 - type: ap value: 14.648781342150446 - type: f1 value: 55.07299194946378 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.919637804187886 - type: f1 value: 61.24122013967399 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.207896583685695 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.23114978840078 - type: cos_sim_ap value: 74.26624727825818 - type: cos_sim_f1 value: 68.72377190817083 - type: cos_sim_precision value: 64.56400742115028 - type: cos_sim_recall value: 73.45646437994723 - type: dot_accuracy value: 86.23114978840078 - type: dot_ap value: 74.26624032659652 - type: dot_f1 value: 68.72377190817083 - type: dot_precision value: 64.56400742115028 - type: dot_recall value: 73.45646437994723 - type: euclidean_accuracy value: 86.23114978840078 - type: euclidean_ap value: 74.26624714480556 - type: euclidean_f1 value: 68.72377190817083 - type: euclidean_precision value: 64.56400742115028 - type: euclidean_recall value: 73.45646437994723 - type: manhattan_accuracy value: 86.16558383501221 - type: manhattan_ap value: 74.2091943976357 - type: manhattan_f1 value: 68.64221520524654 - type: manhattan_precision value: 63.59135913591359 - type: manhattan_recall value: 74.5646437994723 - type: max_accuracy value: 86.23114978840078 - type: max_ap value: 74.26624727825818 - type: max_f1 value: 68.72377190817083 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.3681841114604 - type: cos_sim_ap value: 86.65166387498546 - type: cos_sim_f1 value: 79.02581944698774 - type: cos_sim_precision value: 75.35796605434099 - type: cos_sim_recall value: 83.06898675700647 - type: dot_accuracy value: 89.3681841114604 - type: dot_ap value: 86.65166019802056 - type: dot_f1 value: 79.02581944698774 - type: dot_precision value: 75.35796605434099 - type: dot_recall value: 83.06898675700647 - type: euclidean_accuracy value: 89.3681841114604 - type: euclidean_ap value: 86.65166462876266 - type: euclidean_f1 value: 79.02581944698774 - type: euclidean_precision value: 75.35796605434099 - type: euclidean_recall value: 83.06898675700647 - type: manhattan_accuracy value: 89.36624364497226 - type: manhattan_ap value: 86.65076471274106 - type: manhattan_f1 value: 79.07408783532733 - type: manhattan_precision value: 76.41102972856527 - type: manhattan_recall value: 81.92947336002464 - type: max_accuracy value: 89.3681841114604 - type: max_ap value: 86.65166462876266 - type: max_f1 value: 79.07408783532733 --- # nesall/nomic-embed-text-v1.5-Q6_K-GGUF This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -c 2048 ```
ultratopaz/1321983	ultratopaz	2025-09-05T19:53:38Z	0	null	[ "region:us" ]	null	2025-09-05T19:53:38Z	[View on Civ Archive](https://civarchive.com/models/1258863?modelVersionId=1419421)
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q4_1-SPECIAL_SPLIT	Thireus	2025-09-05T19:53:17Z	4	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-26T04:08:44Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
ultratopaz/1272206	ultratopaz	2025-09-05T19:53:13Z	0	null	[ "region:us" ]	null	2025-09-05T19:53:13Z	[View on Civ Archive](https://civarchive.com/models/1145757?modelVersionId=1369242)
seraphimzzzz/1269773	seraphimzzzz	2025-09-05T19:53:01Z	0	null	[ "region:us" ]	null	2025-09-05T19:53:01Z	[View on Civ Archive](https://civarchive.com/models/1211769?modelVersionId=1364902)
ultratopaz/1248399	ultratopaz	2025-09-05T19:52:49Z	0	null	[ "region:us" ]	null	2025-09-05T19:52:49Z	[View on Civ Archive](https://civarchive.com/models/1193362?modelVersionId=1343608)
amethyst9/1284777	amethyst9	2025-09-05T19:52:32Z	0	null	[ "region:us" ]	null	2025-09-05T19:52:31Z	[View on Civ Archive](https://civarchive.com/models/1225756?modelVersionId=1381044)
Viktor-01/blockassist-bc-leaping_humming_finch_1757099760	Viktor-01	2025-09-05T19:51:47Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "leaping humming finch", "arxiv:2504.07091", "region:us" ]	null	2025-09-05T19:51:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - leaping humming finch --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/1256476	seraphimzzzz	2025-09-05T19:51:40Z	0	null	[ "region:us" ]	null	2025-09-05T19:51:40Z	[View on Civ Archive](https://civarchive.com/models/1200481?modelVersionId=1351765)
ultratopaz/1271408	ultratopaz	2025-09-05T19:51:28Z	0	null	[ "region:us" ]	null	2025-09-05T19:51:28Z	[View on Civ Archive](https://civarchive.com/models/1212847?modelVersionId=1366152)
amethyst9/1293545	amethyst9	2025-09-05T19:51:14Z	0	null	[ "region:us" ]	null	2025-09-05T19:51:14Z	[View on Civ Archive](https://civarchive.com/models/1233536?modelVersionId=1390039)
seraphimzzzz/1283580	seraphimzzzz	2025-09-05T19:51:02Z	0	null	[ "region:us" ]	null	2025-09-05T19:51:02Z	[View on Civ Archive](https://civarchive.com/models/1223637?modelVersionId=1378618)
seraphimzzzz/1253194	seraphimzzzz	2025-09-05T19:50:51Z	0	null	[ "region:us" ]	null	2025-09-05T19:50:51Z	[View on Civ Archive](https://civarchive.com/models/1197280?modelVersionId=1348112)
Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q3_K_R4-SPECIAL_SPLIT	Thireus	2025-09-05T19:49:51Z	0	null	[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-25T18:54:37Z	--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides GGUF-quantized tensors for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections tl;dr: Expand the details section below <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3\|4\|5\|6)\.ffn_.=CUDA0" \ -ot "blk\.(7\|8\|9\|10)\.ffn_.=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL. --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 Download Model Shards – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`. 4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉
ultratopaz/1261009	ultratopaz	2025-09-05T19:49:46Z	0	null	[ "region:us" ]	null	2025-09-05T19:49:46Z	[View on Civ Archive](https://civarchive.com/models/1204451?modelVersionId=1356335)
crystalline7/1292031	crystalline7	2025-09-05T19:49:32Z	0	null	[ "region:us" ]	null	2025-09-05T19:49:32Z	[View on Civ Archive](https://civarchive.com/models/1231282?modelVersionId=1387424)
amethyst9/1236646	amethyst9	2025-09-05T19:49:08Z	0	null	[ "region:us" ]	null	2025-09-05T19:49:06Z	[View on Civ Archive](https://civarchive.com/models/1183550?modelVersionId=1332070)
ultratopaz/1289747	ultratopaz	2025-09-05T19:48:29Z	0	null	[ "region:us" ]	null	2025-09-05T19:48:30Z	[View on Civ Archive](https://civarchive.com/models/1229799?modelVersionId=1385741)
seraphimzzzz/1386320	seraphimzzzz	2025-09-05T19:48:05Z	0	null	[ "region:us" ]	null	2025-09-05T19:48:06Z	[View on Civ Archive](https://civarchive.com/models/1315091?modelVersionId=1484608)
crystalline7/1202072	crystalline7	2025-09-05T19:47:38Z	0	null	[ "region:us" ]	null	2025-09-05T19:47:35Z	[View on Civ Archive](https://civarchive.com/models/1150650?modelVersionId=1294177)
seraphimzzzz/1267523	seraphimzzzz	2025-09-05T19:47:23Z	0	null	[ "region:us" ]	null	2025-09-05T19:47:23Z	[View on Civ Archive](https://civarchive.com/models/1209317?modelVersionId=1362024)
mamersfo/Llama-3.2-3B-Instruct-sft-hr	mamersfo	2025-09-05T19:46:49Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-05T19:46:39Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mamersfo - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
amethyst9/1223596	amethyst9	2025-09-05T19:46:47Z	0	null	[ "region:us" ]	null	2025-09-05T19:46:47Z	[View on Civ Archive](https://civarchive.com/models/1171788?modelVersionId=1318414)
seraphimzzzz/1301630	seraphimzzzz	2025-09-05T19:46:36Z	0	null	[ "region:us" ]	null	2025-09-05T19:46:36Z	[View on Civ Archive](https://civarchive.com/models/1240588?modelVersionId=1398212)

Thireus/Qwen3-4B-Thinking-2507-THIREUS-Q5_K-SPECIAL_SPLIT

Thireus

2025-09-05T20:23:29Z

3

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-29T05:51:43Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

fopppyu/blockassist-bc-stinky_stinky_cassowary_1757103700

fopppyu

2025-09-05T20:22:41Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinky stinky cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:21:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinky stinky cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

BrinnTano/jarvis-deepsearch

BrinnTano

2025-09-05T20:22:33Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-09-05T20:22:33Z

--- license: apache-2.0 ---

bah63843/blockassist-bc-plump_fast_antelope_1757103621

bah63843

2025-09-05T20:21:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:21:08Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ1_M-SPECIAL_SPLIT

Thireus

2025-09-05T20:21:46Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-29T05:56:38Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ5_K_R4-SPECIAL_SPLIT

Thireus

2025-09-05T20:21:11Z

3

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-29T05:54:11Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ4_XS-SPECIAL_SPLIT

Thireus

2025-09-05T20:20:42Z

3

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-29T05:54:18Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

seraphimzzzz/1614495

seraphimzzzz

2025-09-05T20:20:09Z

0

null

[ "region:us" ]

null

2025-09-05T20:20:09Z

[View on Civ Archive](https://civarchive.com/models/1380495?modelVersionId=1561100)

ultratopaz/1614478

ultratopaz

2025-09-05T20:19:59Z

0

null

[ "region:us" ]

null

2025-09-05T20:20:02Z

[View on Civ Archive](https://civarchive.com/models/1449650?modelVersionId=1639054)

Soya2305/ppo-LunarLander-v3

Soya2305

2025-09-05T20:19:48Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-09-05T19:54:24Z

--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: 255.71 +/- 20.27 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v3** This is a trained model of a **PPO** agent playing **LunarLander-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

crystalline7/1069413

crystalline7

2025-09-05T20:19:43Z

0

null

[ "region:us" ]

null

2025-09-05T20:19:46Z

[View on Civ Archive](https://civarchive.com/models/1037884?modelVersionId=1164190)

seraphimzzzz/1614485

seraphimzzzz

2025-09-05T20:19:31Z

0

null

[ "region:us" ]

null

2025-09-05T20:19:33Z

[View on Civ Archive](https://civarchive.com/models/1438138?modelVersionId=1625671)

amethyst9/1070492

amethyst9

2025-09-05T20:19:05Z

0

null

[ "region:us" ]

null

2025-09-05T20:19:08Z

[View on Civ Archive](https://civarchive.com/models/1038796?modelVersionId=1165262)

amethyst9/1167553

amethyst9

2025-09-05T20:18:37Z

0

null

[ "region:us" ]

null

2025-09-05T20:18:40Z

[View on Civ Archive](https://civarchive.com/models/1123223?modelVersionId=1262447)

crystalline7/1614476

crystalline7

2025-09-05T20:18:30Z

0

null

[ "region:us" ]

null

2025-09-05T20:18:33Z

[View on Civ Archive](https://civarchive.com/models/1437649?modelVersionId=1625115)

Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ2_KS-SPECIAL_SPLIT

Thireus

2025-09-05T20:18:24Z

3

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-28T23:55:46Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

crystalline7/1614504

crystalline7

2025-09-05T20:18:16Z

0

null

[ "region:us" ]

null

2025-09-05T20:18:19Z

[View on Civ Archive](https://civarchive.com/models/1038258?modelVersionId=1164624)

amethyst9/1614506

amethyst9

2025-09-05T20:18:12Z

0

null

[ "region:us" ]

null

2025-09-05T20:18:12Z

[View on Civ Archive](https://civarchive.com/models/1426394?modelVersionId=1612237)

crystalline7/983283

crystalline7

2025-09-05T20:18:02Z

0

null

[ "region:us" ]

null

2025-09-05T20:18:05Z

[View on Civ Archive](https://civarchive.com/models/963042?modelVersionId=1078217)

ultratopaz/1614479

ultratopaz

2025-09-05T20:17:46Z

0

null

[ "region:us" ]

null

2025-09-05T20:17:45Z

[View on Civ Archive](https://civarchive.com/models/1449650?modelVersionId=1639022)

ultratopaz/1614489

ultratopaz

2025-09-05T20:17:33Z

0

null

[ "region:us" ]

null

2025-09-05T20:17:33Z

[View on Civ Archive](https://civarchive.com/models/1380503?modelVersionId=1653825)

ultratopaz/1070360

ultratopaz

2025-09-05T20:17:15Z

0

null

[ "region:us" ]

null

2025-09-05T20:17:05Z

[View on Civ Archive](https://civarchive.com/models/1038694?modelVersionId=1165139)

seraphimzzzz/1614501

seraphimzzzz

2025-09-05T20:16:38Z

0

null

[ "region:us" ]

null

2025-09-05T20:16:36Z

[View on Civ Archive](https://civarchive.com/models/993331?modelVersionId=1112961)

ultratopaz/1614470

ultratopaz

2025-09-05T20:16:17Z

0

null

[ "region:us" ]

null

2025-09-05T20:16:20Z

[View on Civ Archive](https://civarchive.com/models/1069541?modelVersionId=1200489)

trongg/5a1f05b4-a01a-486d-b95c-ff2164c0b463

trongg

2025-09-05T20:16:01Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-05T19:48:56Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ1_M_R4-SPECIAL_SPLIT

Thireus

2025-09-05T20:15:38Z

3

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-28T23:42:34Z

--- license: mit --- # Qwen3-4B-Thinking-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/Qwen3-4B-Thinking-2507/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3-4B-Thinking-2507.ROOT-4.2498bpw-10.9335ppl.1GB-GGUF_0GB-GPU_1GB-CPU.9888e4b_9193781.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-server \ -m Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \ -fa -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \ -b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \ --main-gpu 0 ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how Qwen3-4B-Thinking-2507 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3-4B-Thinking-2507.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

amethyst9/980180

amethyst9

2025-09-05T20:15:33Z

0

null

[ "region:us" ]

null

2025-09-05T20:15:32Z

[View on Civ Archive](https://civarchive.com/models/960215?modelVersionId=1075070)

fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757103268

fakir22

2025-09-05T20:15:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping peaceful caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:15:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping peaceful caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

bah63843/blockassist-bc-plump_fast_antelope_1757103250

bah63843

2025-09-05T20:15:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:14:52Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

TrinitySkye/TrintySkye-Replicate

TrinitySkye

2025-09-05T20:14:54Z

2

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-09-04T17:17:05Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Trinity --- # Trintyskye Replicate <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Trinity` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Trinity", "lora_weights": "https://huggingface.co/TrinitySkye/TrintySkye-Replicate/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('TrinitySkye/TrintySkye-Replicate', weight_name='lora.safetensors') image = pipeline('Trinity').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2008 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/TrinitySkye/TrintySkye-Replicate/discussions) to add images that show off what you’ve made with this LoRA.

amethyst9/1475260

amethyst9

2025-09-05T20:14:52Z

0

null

[ "region:us" ]

null

2025-09-05T20:14:48Z

[View on Civ Archive](https://civarchive.com/models/1393873?modelVersionId=1575460)

Miracle-man/blockassist-bc-singing_lithe_koala_1757101522

Miracle-man

2025-09-05T20:14:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing lithe koala", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:14:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing lithe koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vennertou/blockassist-bc-durable_rangy_elephant_1757103204

vennertou

2025-09-05T20:14:11Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "durable rangy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:13:24Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - durable rangy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

grizzle00/blockassist-bc-curious_mimic_antelope_1757101009

grizzle00

2025-09-05T20:13:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "curious mimic antelope", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:13:22Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - curious mimic antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mradermacher/L3-8B-Lunaris-cl-i1-GGUF

mradermacher

2025-09-05T20:13:13Z

0

transformers

[ "transformers", "gguf", "en", "base_model:dinalad0/L3-8B-Lunaris-cl", "base_model:quantized:dinalad0/L3-8B-Lunaris-cl", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-09-05T17:10:09Z

--- base_model: dinalad0/L3-8B-Lunaris-cl language: - en library_name: transformers license: llama3 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         weighted/imatrix quants of https://huggingface.co/dinalad0/L3-8B-Lunaris-cl  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#L3-8B-Lunaris-cl-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Lunaris-cl-i1-GGUF/resolve/main/L3-8B-Lunaris-cl.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mekala-2402/Model0-Q4_K_M-GGUF

mekala-2402

2025-09-05T20:12:53Z

0

null

[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:mekala-2402/Model0", "base_model:quantized:mekala-2402/Model0", "endpoints_compatible", "region:us" ]

null

2025-09-05T20:12:40Z

--- base_model: mekala-2402/Model0 tags: - llama-cpp - gguf-my-repo --- # mekala-2402/Model0-Q4_K_M-GGUF This model was converted to GGUF format from [`mekala-2402/Model0`](https://huggingface.co/mekala-2402/Model0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mekala-2402/Model0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mekala-2402/Model0-Q4_K_M-GGUF --hf-file model0-q4_k_m.gguf -c 2048 ```

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-IQ1_BN-SPECIAL_SPLIT

Thireus

2025-09-05T20:11:19Z

5

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "region:us" ]

null

2025-07-24T06:32:00Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

cwayneconnor/blockassist-bc-mute_loud_lynx_1757102815

cwayneconnor

2025-09-05T20:11:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute loud lynx", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:08:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute loud lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

popouy/blockassist-bc-mute_whistling_hamster_1757103017

popouy

2025-09-05T20:10:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute whistling hamster", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:10:18Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute whistling hamster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

koloni/blockassist-bc-deadly_graceful_stingray_1757101473

koloni

2025-09-05T20:10:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:10:24Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1757101312

kojeklollipop

2025-09-05T20:10:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:10:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-IQ2_KT-SPECIAL_SPLIT

Thireus

2025-09-05T20:08:54Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-24T22:37:42Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

canoplos112/blockassist-bc-yapping_sleek_squirrel_1757102769

canoplos112

2025-09-05T20:08:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:06:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

popouy/blockassist-bc-prowling_aquatic_baboon_1757102781

popouy

2025-09-05T20:06:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prowling aquatic baboon", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:06:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prowling aquatic baboon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q8_K_R8-SPECIAL_SPLIT

Thireus

2025-09-05T20:06:32Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:10:06Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

bah63843/blockassist-bc-plump_fast_antelope_1757102689

bah63843

2025-09-05T20:05:49Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:05:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q8_0_R8-SPECIAL_SPLIT

Thireus

2025-09-05T20:05:22Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:09:59Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

rettertop/blockassist-bc-miniature_enormous_dolphin_1757102661

rettertop

2025-09-05T20:04:57Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "miniature enormous dolphin", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:04:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - miniature enormous dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

18-Sophie-Rain-Spider-Man-Vi-deo-Tutori-al/Sophie.Rain.Spiderman.Video.Tutorial

18-Sophie-Rain-Spider-Man-Vi-deo-Tutori-al

2025-09-05T20:03:17Z

0

null

[ "region:us" ]

null

2025-09-05T19:58:31Z

<div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> </div>

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q6_0_R4-SPECIAL_SPLIT

Thireus

2025-09-05T20:01:43Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:09:32Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

bah63843/blockassist-bc-plump_fast_antelope_1757102374

bah63843

2025-09-05T20:00:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:00:15Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q6_0-SPECIAL_SPLIT

Thireus

2025-09-05T20:00:29Z

1

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:09:25Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

popouy/blockassist-bc-amphibious_slender_dingo_1757102402

popouy

2025-09-05T20:00:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious slender dingo", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T20:00:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious slender dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ghosthash1/AlphaNeural

ghosthash1

2025-09-05T19:59:57Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Qwen/Qwen-Image", "base_model:adapter:Qwen/Qwen-Image", "license:apache-2.0", "region:us" ]

text-to-image

2025-09-05T19:59:55Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/Unknown-1.jpeg text: '-' base_model: Qwen/Qwen-Image instance_prompt: null license: apache-2.0 --- # AlphaNeural <Gallery /> ## Download model [Download](/ghosthash1/AlphaNeural/tree/main) them in the Files & versions tab.

moyixiao/Qwen3-0.6B-bnpo-f162-100

moyixiao

2025-09-05T19:59:37Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-05T19:59:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q5_K_R4-SPECIAL_SPLIT

Thireus

2025-09-05T19:59:16Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:09:18Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

ultratopaz/1258929

ultratopaz

2025-09-05T19:58:47Z

0

null

[ "region:us" ]

null

2025-09-05T19:58:48Z

[View on Civ Archive](https://civarchive.com/models/1201559?modelVersionId=1352969)

crystalline7/1296403

crystalline7

2025-09-05T19:58:36Z

0

null

[ "region:us" ]

null

2025-09-05T19:58:36Z

[View on Civ Archive](https://civarchive.com/models/1235078?modelVersionId=1391868)

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q5_1-SPECIAL_SPLIT

Thireus

2025-09-05T19:58:02Z

1

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:09:12Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

beyoru/Qwen3-M2N2-Dynamic

beyoru

2025-09-05T19:57:45Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "agent", "math", "code", "assistant", "merge", "conversational", "base_model:unsloth/Qwen3-0.6B", "base_model:finetune:unsloth/Qwen3-0.6B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-09-05T06:33:42Z

--- library_name: transformers tags: - agent - math - code - assistant - merge base_model: - unsloth/Qwen3-0.6B --- # Model: Qwen3-M2N2-Dynamic ## Model Overview **Qwen3-M2N2-Dynamic** is an extension of `beyoru/Qwen3-M2N2`, designed to enhance evolutionary computation with dynamic population management and parent selection heuristics. The development occurs in two stages: **Stage 1: Qwen3-M2N2** * Implements competition sharing (`evaluate_population` with share allocation). * Supports M2N2 merge (SLERP + split). * Boundary remains static (single point based on `W_s`). * Parent selection relies on top-2 fitness only; no attraction heuristic. **Stage 2: Qwen3-M2N2-Dynamic** * Introduces dynamic split for more flexible boundary handling. * Implements attraction-based parent selection. * Enhances population diversity. --- ## Intended Use * Research in evolutionary algorithms and population-based optimization. * Comparison and benchmarking against baseline models like `Qwen3-0.6B`. * Use in both single-turn and multi-turn function evaluation scenarios. --- ## Evaluation ### Agent Performance *non reasoning for both model* The table summarizes model performance across different test categories: | Test Category | Qwen3-M2N2-Dynamic | Qwen3-0.6B | P1 | P2 | | ------------------------------------ | ------------------ | ---------- | ---------- | ----------- | | Normal Single Turn Single Function | **0.26** | 0.19 | 0.24 | 0.30 | | Normal Single Turn Parallel Function | 0.03 | **0.09** | 0.03 | 0.04 | | Normal Multi Turn User Adjust | 0.08 | 0.00 | 0.02 | 0.12 | | Normal Multi Turn User Switch | 0.10 | 0.02 | 0.08 | 0.12 | | Normal Similar API | 0.34 | 0.40 | 0.34 | 0.42 | | Normal Preference | **0.14** | 0.10 | 0.12 | 0.14 | | Normal Atom Bool | **0.46** | 0.32 | 0.46 | 0.48 | | Normal Atom Enum | **0.42** | 0.26 | 0.42 | 0.50 | | Normal Atom Number | **0.46** | 0.40 | 0.40 | 0.46 | | Normal Atom List | **0.48** | 0.28 | 0.44 | 0.54 | | Normal Atom Object Deep | 0.12 | 0.12 | 0.10 | 0.14 | | Normal Atom Object Short | 0.28 | 0.28 | 0.28 | 0.38 | | **Overall** | **0.27** | 0.20 | 0.24 | 0.33 | --- *on-going test more* ## Limitations * Static boundary selection remains in Stage 1. * Stage 2 introduces dynamic split, but performance varies by task category. * Attraction heuristic may bias parent selection toward high-fitness individuals, potentially reducing exploration. * This is only a test version, so only a small set to used for training. ---

amethyst9/1275521

amethyst9

2025-09-05T19:57:24Z

0

null

[ "region:us" ]

null

2025-09-05T19:57:24Z

[View on Civ Archive](https://civarchive.com/models/1215999?modelVersionId=1369769)

ultratopaz/1219846

ultratopaz

2025-09-05T19:57:12Z

0

null

[ "region:us" ]

null

2025-09-05T19:57:13Z

[View on Civ Archive](https://civarchive.com/models/1168489?modelVersionId=1314603)

seraphimzzzz/1318223

seraphimzzzz

2025-09-05T19:57:00Z

0

null

[ "region:us" ]

null

2025-09-05T19:57:01Z

[View on Civ Archive](https://civarchive.com/models/1255580?modelVersionId=1415579)

amethyst9/1330428

amethyst9

2025-09-05T19:56:48Z

0

null

[ "region:us" ]

null

2025-09-05T19:56:49Z

[View on Civ Archive](https://civarchive.com/models/1266054?modelVersionId=1427903)

ultratopaz/1285684

ultratopaz

2025-09-05T19:56:36Z

0

null

[ "region:us" ]

null

2025-09-05T19:56:37Z

[View on Civ Archive](https://civarchive.com/models/1226063?modelVersionId=1381426)

popouy/blockassist-bc-bellowing_mammalian_camel_1757102167

popouy

2025-09-05T19:56:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing mammalian camel", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T19:56:07Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing mammalian camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

gopterwegop/blockassist-bc-beaked_exotic_spider_1757102160

gopterwegop

2025-09-05T19:56:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked exotic spider", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T19:56:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked exotic spider --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

kafa22/blockassist-bc-regal_leggy_hummingbird_1757102137

kafa22

2025-09-05T19:56:18Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal leggy hummingbird", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T19:56:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal leggy hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ultratopaz/1252582

ultratopaz

2025-09-05T19:56:12Z

0

null

[ "region:us" ]

null

2025-09-05T19:56:13Z

[View on Civ Archive](https://civarchive.com/models/1145757?modelVersionId=1349097)

qgallouedec/Qwen3-8B-SFT-20250905191102

qgallouedec

2025-09-05T19:56:01Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "hf_jobs", "trl", "sft", "dataset:trl-lib/Capybara", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "endpoints_compatible", "region:us" ]

null

2025-09-05T19:11:55Z

--- base_model: Qwen/Qwen3-8B datasets: trl-lib/Capybara library_name: transformers model_name: Qwen3-8B-SFT-20250905191102 tags: - generated_from_trainer - hf_jobs - trl - sft licence: license --- # Model Card for Qwen3-8B-SFT-20250905191102 This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="qgallouedec/Qwen3-8B-SFT-20250905191102", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0.dev0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu128 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ultratopaz/1259974

ultratopaz

2025-09-05T19:55:36Z

0

null

[ "region:us" ]

null

2025-09-05T19:55:36Z

[View on Civ Archive](https://civarchive.com/models/1202079?modelVersionId=1353577)

hellonmn/llama3-code-generator

hellonmn

2025-09-05T19:55:15Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-05T19:54:42Z

--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** hellonmn - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

crystalline7/1304358

crystalline7

2025-09-05T19:55:12Z

0

null

[ "region:us" ]

null

2025-09-05T19:55:12Z

[View on Civ Archive](https://civarchive.com/models/1241157?modelVersionId=1398842)

golopper/blockassist-bc-lithe_hulking_wasp_1757102035

golopper

2025-09-05T19:54:13Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lithe hulking wasp", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T19:53:56Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lithe hulking wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

amethyst9/1244045

amethyst9

2025-09-05T19:54:02Z

0

null

[ "region:us" ]

null

2025-09-05T19:54:02Z

[View on Civ Archive](https://civarchive.com/models/1189552?modelVersionId=1339185)

crystalline7/1229895

crystalline7

2025-09-05T19:53:49Z

0

null

[ "region:us" ]

null

2025-09-05T19:53:49Z

[View on Civ Archive](https://civarchive.com/models/1176514?modelVersionId=1323817)

nesall/nomic-embed-text-v1.5-Q6_K-GGUF

nesall

2025-09-05T19:53:48Z

0

sentence-transformers

[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js", "llama-cpp", "gguf-my-repo", "en", "base_model:nomic-ai/nomic-embed-text-v1.5", "base_model:quantized:nomic-ai/nomic-embed-text-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-09-05T19:53:44Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - mteb - transformers - transformers.js - llama-cpp - gguf-my-repo license: apache-2.0 language: - en base_model: nomic-ai/nomic-embed-text-v1.5 model-index: - name: epoch_0_model results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.20895522388058 - type: ap value: 38.57605549557802 - type: f1 value: 69.35586565857854 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.8144 - type: ap value: 88.65222882032363 - type: f1 value: 91.80426301643274 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.162000000000006 - type: f1 value: 46.59329642263158 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 24.253 - type: map_at_10 value: 38.962 - type: map_at_100 value: 40.081 - type: map_at_1000 value: 40.089000000000006 - type: map_at_3 value: 33.499 - type: map_at_5 value: 36.351 - type: mrr_at_1 value: 24.609 - type: mrr_at_10 value: 39.099000000000004 - type: mrr_at_100 value: 40.211000000000006 - type: mrr_at_1000 value: 40.219 - type: mrr_at_3 value: 33.677 - type: mrr_at_5 value: 36.469 - type: ndcg_at_1 value: 24.253 - type: ndcg_at_10 value: 48.010999999999996 - type: ndcg_at_100 value: 52.756 - type: ndcg_at_1000 value: 52.964999999999996 - type: ndcg_at_3 value: 36.564 - type: ndcg_at_5 value: 41.711999999999996 - type: precision_at_1 value: 24.253 - type: precision_at_10 value: 7.738 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.149000000000001 - type: precision_at_5 value: 11.593 - type: recall_at_1 value: 24.253 - type: recall_at_10 value: 77.383 - type: recall_at_100 value: 98.009 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 45.448 - type: recall_at_5 value: 57.965999999999994 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.69069567851087 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.35185490976283 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.71274951450321 - type: mrr value: 76.06032625423207 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.73980520022269 - type: cos_sim_spearman value: 84.24649792685918 - type: euclidean_pearson value: 85.85197641158186 - type: euclidean_spearman value: 84.24649792685918 - type: manhattan_pearson value: 86.26809552711346 - type: manhattan_spearman value: 84.56397504030865 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.25324675324674 - type: f1 value: 84.17872280892557 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.770253446400886 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.94307095497281 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.164 - type: map_at_10 value: 42.641 - type: map_at_100 value: 43.947 - type: map_at_1000 value: 44.074999999999996 - type: map_at_3 value: 39.592 - type: map_at_5 value: 41.204 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 48.625 - type: mrr_at_100 value: 49.368 - type: mrr_at_1000 value: 49.413000000000004 - type: mrr_at_3 value: 46.400000000000006 - type: mrr_at_5 value: 47.68 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 48.564 - type: ndcg_at_100 value: 53.507000000000005 - type: ndcg_at_1000 value: 55.635999999999996 - type: ndcg_at_3 value: 44.471 - type: ndcg_at_5 value: 46.137 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 8.856 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 14.649000000000001 - type: recall_at_1 value: 32.164 - type: recall_at_10 value: 59.609 - type: recall_at_100 value: 80.521 - type: recall_at_1000 value: 94.245 - type: recall_at_3 value: 46.521 - type: recall_at_5 value: 52.083999999999996 - type: map_at_1 value: 31.526 - type: map_at_10 value: 41.581 - type: map_at_100 value: 42.815999999999995 - type: map_at_1000 value: 42.936 - type: map_at_3 value: 38.605000000000004 - type: map_at_5 value: 40.351 - type: mrr_at_1 value: 39.489999999999995 - type: mrr_at_10 value: 47.829 - type: mrr_at_100 value: 48.512 - type: mrr_at_1000 value: 48.552 - type: mrr_at_3 value: 45.754 - type: mrr_at_5 value: 46.986 - type: ndcg_at_1 value: 39.489999999999995 - type: ndcg_at_10 value: 47.269 - type: ndcg_at_100 value: 51.564 - type: ndcg_at_1000 value: 53.53099999999999 - type: ndcg_at_3 value: 43.301 - type: ndcg_at_5 value: 45.239000000000004 - type: precision_at_1 value: 39.489999999999995 - type: precision_at_10 value: 8.93 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 20.892 - type: precision_at_5 value: 14.865999999999998 - type: recall_at_1 value: 31.526 - type: recall_at_10 value: 56.76 - type: recall_at_100 value: 75.029 - type: recall_at_1000 value: 87.491 - type: recall_at_3 value: 44.786 - type: recall_at_5 value: 50.254 - type: map_at_1 value: 40.987 - type: map_at_10 value: 52.827 - type: map_at_100 value: 53.751000000000005 - type: map_at_1000 value: 53.81 - type: map_at_3 value: 49.844 - type: map_at_5 value: 51.473 - type: mrr_at_1 value: 46.833999999999996 - type: mrr_at_10 value: 56.389 - type: mrr_at_100 value: 57.003 - type: mrr_at_1000 value: 57.034 - type: mrr_at_3 value: 54.17999999999999 - type: mrr_at_5 value: 55.486999999999995 - type: ndcg_at_1 value: 46.833999999999996 - type: ndcg_at_10 value: 58.372 - type: ndcg_at_100 value: 62.068 - type: ndcg_at_1000 value: 63.288 - type: ndcg_at_3 value: 53.400000000000006 - type: ndcg_at_5 value: 55.766000000000005 - type: precision_at_1 value: 46.833999999999996 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.448 - type: precision_at_5 value: 15.862000000000002 - type: recall_at_1 value: 40.987 - type: recall_at_10 value: 71.146 - type: recall_at_100 value: 87.035 - type: recall_at_1000 value: 95.633 - type: recall_at_3 value: 58.025999999999996 - type: recall_at_5 value: 63.815999999999995 - type: map_at_1 value: 24.587 - type: map_at_10 value: 33.114 - type: map_at_100 value: 34.043 - type: map_at_1000 value: 34.123999999999995 - type: map_at_3 value: 30.45 - type: map_at_5 value: 31.813999999999997 - type: mrr_at_1 value: 26.554 - type: mrr_at_10 value: 35.148 - type: mrr_at_100 value: 35.926 - type: mrr_at_1000 value: 35.991 - type: mrr_at_3 value: 32.599000000000004 - type: mrr_at_5 value: 33.893 - type: ndcg_at_1 value: 26.554 - type: ndcg_at_10 value: 38.132 - type: ndcg_at_100 value: 42.78 - type: ndcg_at_1000 value: 44.919 - type: ndcg_at_3 value: 32.833 - type: ndcg_at_5 value: 35.168 - type: precision_at_1 value: 26.554 - type: precision_at_10 value: 5.921 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.861 - type: precision_at_5 value: 9.605 - type: recall_at_1 value: 24.587 - type: recall_at_10 value: 51.690000000000005 - type: recall_at_100 value: 73.428 - type: recall_at_1000 value: 89.551 - type: recall_at_3 value: 37.336999999999996 - type: recall_at_5 value: 43.047000000000004 - type: map_at_1 value: 16.715 - type: map_at_10 value: 24.251 - type: map_at_100 value: 25.326999999999998 - type: map_at_1000 value: 25.455 - type: map_at_3 value: 21.912000000000003 - type: map_at_5 value: 23.257 - type: mrr_at_1 value: 20.274 - type: mrr_at_10 value: 28.552 - type: mrr_at_100 value: 29.42 - type: mrr_at_1000 value: 29.497 - type: mrr_at_3 value: 26.14 - type: mrr_at_5 value: 27.502 - type: ndcg_at_1 value: 20.274 - type: ndcg_at_10 value: 29.088 - type: ndcg_at_100 value: 34.293 - type: ndcg_at_1000 value: 37.271 - type: ndcg_at_3 value: 24.708 - type: ndcg_at_5 value: 26.809 - type: precision_at_1 value: 20.274 - type: precision_at_10 value: 5.361 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.733 - type: precision_at_5 value: 8.556999999999999 - type: recall_at_1 value: 16.715 - type: recall_at_10 value: 39.587 - type: recall_at_100 value: 62.336000000000006 - type: recall_at_1000 value: 83.453 - type: recall_at_3 value: 27.839999999999996 - type: recall_at_5 value: 32.952999999999996 - type: map_at_1 value: 28.793000000000003 - type: map_at_10 value: 38.582 - type: map_at_100 value: 39.881 - type: map_at_1000 value: 39.987 - type: map_at_3 value: 35.851 - type: map_at_5 value: 37.289 - type: mrr_at_1 value: 34.455999999999996 - type: mrr_at_10 value: 43.909 - type: mrr_at_100 value: 44.74 - type: mrr_at_1000 value: 44.786 - type: mrr_at_3 value: 41.659 - type: mrr_at_5 value: 43.010999999999996 - type: ndcg_at_1 value: 34.455999999999996 - type: ndcg_at_10 value: 44.266 - type: ndcg_at_100 value: 49.639 - type: ndcg_at_1000 value: 51.644 - type: ndcg_at_3 value: 39.865 - type: ndcg_at_5 value: 41.887 - type: precision_at_1 value: 34.455999999999996 - type: precision_at_10 value: 7.843999999999999 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 18.831999999999997 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 28.793000000000003 - type: recall_at_10 value: 55.68300000000001 - type: recall_at_100 value: 77.99000000000001 - type: recall_at_1000 value: 91.183 - type: recall_at_3 value: 43.293 - type: recall_at_5 value: 48.618 - type: map_at_1 value: 25.907000000000004 - type: map_at_10 value: 35.519 - type: map_at_100 value: 36.806 - type: map_at_1000 value: 36.912 - type: map_at_3 value: 32.748 - type: map_at_5 value: 34.232 - type: mrr_at_1 value: 31.621 - type: mrr_at_10 value: 40.687 - type: mrr_at_100 value: 41.583 - type: mrr_at_1000 value: 41.638999999999996 - type: mrr_at_3 value: 38.527 - type: mrr_at_5 value: 39.612 - type: ndcg_at_1 value: 31.621 - type: ndcg_at_10 value: 41.003 - type: ndcg_at_100 value: 46.617999999999995 - type: ndcg_at_1000 value: 48.82 - type: ndcg_at_3 value: 36.542 - type: ndcg_at_5 value: 38.368 - type: precision_at_1 value: 31.621 - type: precision_at_10 value: 7.396999999999999 - type: precision_at_100 value: 1.191 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 17.39 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 25.907000000000004 - type: recall_at_10 value: 52.115 - type: recall_at_100 value: 76.238 - type: recall_at_1000 value: 91.218 - type: recall_at_3 value: 39.417 - type: recall_at_5 value: 44.435 - type: map_at_1 value: 25.732166666666668 - type: map_at_10 value: 34.51616666666667 - type: map_at_100 value: 35.67241666666666 - type: map_at_1000 value: 35.78675 - type: map_at_3 value: 31.953416666666662 - type: map_at_5 value: 33.333 - type: mrr_at_1 value: 30.300166666666673 - type: mrr_at_10 value: 38.6255 - type: mrr_at_100 value: 39.46183333333334 - type: mrr_at_1000 value: 39.519999999999996 - type: mrr_at_3 value: 36.41299999999999 - type: mrr_at_5 value: 37.6365 - type: ndcg_at_1 value: 30.300166666666673 - type: ndcg_at_10 value: 39.61466666666667 - type: ndcg_at_100 value: 44.60808333333334 - type: ndcg_at_1000 value: 46.91708333333334 - type: ndcg_at_3 value: 35.26558333333333 - type: ndcg_at_5 value: 37.220000000000006 - type: precision_at_1 value: 30.300166666666673 - type: precision_at_10 value: 6.837416666666667 - type: precision_at_100 value: 1.10425 - type: precision_at_1000 value: 0.14875 - type: precision_at_3 value: 16.13716666666667 - type: precision_at_5 value: 11.2815 - type: recall_at_1 value: 25.732166666666668 - type: recall_at_10 value: 50.578916666666665 - type: recall_at_100 value: 72.42183333333334 - type: recall_at_1000 value: 88.48766666666667 - type: recall_at_3 value: 38.41325 - type: recall_at_5 value: 43.515750000000004 - type: map_at_1 value: 23.951 - type: map_at_10 value: 30.974 - type: map_at_100 value: 31.804 - type: map_at_1000 value: 31.900000000000002 - type: map_at_3 value: 28.762 - type: map_at_5 value: 29.94 - type: mrr_at_1 value: 26.534000000000002 - type: mrr_at_10 value: 33.553 - type: mrr_at_100 value: 34.297 - type: mrr_at_1000 value: 34.36 - type: mrr_at_3 value: 31.391000000000002 - type: mrr_at_5 value: 32.525999999999996 - type: ndcg_at_1 value: 26.534000000000002 - type: ndcg_at_10 value: 35.112 - type: ndcg_at_100 value: 39.28 - type: ndcg_at_1000 value: 41.723 - type: ndcg_at_3 value: 30.902 - type: ndcg_at_5 value: 32.759 - type: precision_at_1 value: 26.534000000000002 - type: precision_at_10 value: 5.445 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.951 - type: recall_at_10 value: 45.24 - type: recall_at_100 value: 64.12299999999999 - type: recall_at_1000 value: 82.28999999999999 - type: recall_at_3 value: 33.806000000000004 - type: recall_at_5 value: 38.277 - type: map_at_1 value: 16.829 - type: map_at_10 value: 23.684 - type: map_at_100 value: 24.683 - type: map_at_1000 value: 24.81 - type: map_at_3 value: 21.554000000000002 - type: map_at_5 value: 22.768 - type: mrr_at_1 value: 20.096 - type: mrr_at_10 value: 27.230999999999998 - type: mrr_at_100 value: 28.083999999999996 - type: mrr_at_1000 value: 28.166000000000004 - type: mrr_at_3 value: 25.212 - type: mrr_at_5 value: 26.32 - type: ndcg_at_1 value: 20.096 - type: ndcg_at_10 value: 27.989000000000004 - type: ndcg_at_100 value: 32.847 - type: ndcg_at_1000 value: 35.896 - type: ndcg_at_3 value: 24.116 - type: ndcg_at_5 value: 25.964 - type: precision_at_1 value: 20.096 - type: precision_at_10 value: 5 - type: precision_at_100 value: 0.8750000000000001 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 11.207 - type: precision_at_5 value: 8.08 - type: recall_at_1 value: 16.829 - type: recall_at_10 value: 37.407000000000004 - type: recall_at_100 value: 59.101000000000006 - type: recall_at_1000 value: 81.024 - type: recall_at_3 value: 26.739 - type: recall_at_5 value: 31.524 - type: map_at_1 value: 24.138 - type: map_at_10 value: 32.275999999999996 - type: map_at_100 value: 33.416000000000004 - type: map_at_1000 value: 33.527 - type: map_at_3 value: 29.854000000000003 - type: map_at_5 value: 31.096 - type: mrr_at_1 value: 28.450999999999997 - type: mrr_at_10 value: 36.214 - type: mrr_at_100 value: 37.134 - type: mrr_at_1000 value: 37.198 - type: mrr_at_3 value: 34.001999999999995 - type: mrr_at_5 value: 35.187000000000005 - type: ndcg_at_1 value: 28.450999999999997 - type: ndcg_at_10 value: 37.166 - type: ndcg_at_100 value: 42.454 - type: ndcg_at_1000 value: 44.976 - type: ndcg_at_3 value: 32.796 - type: ndcg_at_5 value: 34.631 - type: precision_at_1 value: 28.450999999999997 - type: precision_at_10 value: 6.241 - type: precision_at_100 value: 0.9950000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.801 - type: precision_at_5 value: 10.280000000000001 - type: recall_at_1 value: 24.138 - type: recall_at_10 value: 48.111 - type: recall_at_100 value: 71.245 - type: recall_at_1000 value: 88.986 - type: recall_at_3 value: 36.119 - type: recall_at_5 value: 40.846 - type: map_at_1 value: 23.244 - type: map_at_10 value: 31.227 - type: map_at_100 value: 33.007 - type: map_at_1000 value: 33.223 - type: map_at_3 value: 28.924 - type: map_at_5 value: 30.017 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 35.524 - type: mrr_at_100 value: 36.699 - type: mrr_at_1000 value: 36.759 - type: mrr_at_3 value: 33.366 - type: mrr_at_5 value: 34.552 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 36.381 - type: ndcg_at_100 value: 43.062 - type: ndcg_at_1000 value: 45.656 - type: ndcg_at_3 value: 32.501999999999995 - type: ndcg_at_5 value: 34.105999999999995 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 6.798 - type: precision_at_100 value: 1.492 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 15.152 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.244 - type: recall_at_10 value: 45.979 - type: recall_at_100 value: 74.822 - type: recall_at_1000 value: 91.078 - type: recall_at_3 value: 34.925 - type: recall_at_5 value: 39.126 - type: map_at_1 value: 19.945 - type: map_at_10 value: 27.517999999999997 - type: map_at_100 value: 28.588 - type: map_at_1000 value: 28.682000000000002 - type: map_at_3 value: 25.345000000000002 - type: map_at_5 value: 26.555 - type: mrr_at_1 value: 21.996 - type: mrr_at_10 value: 29.845 - type: mrr_at_100 value: 30.775999999999996 - type: mrr_at_1000 value: 30.845 - type: mrr_at_3 value: 27.726 - type: mrr_at_5 value: 28.882 - type: ndcg_at_1 value: 21.996 - type: ndcg_at_10 value: 32.034 - type: ndcg_at_100 value: 37.185 - type: ndcg_at_1000 value: 39.645 - type: ndcg_at_3 value: 27.750999999999998 - type: ndcg_at_5 value: 29.805999999999997 - type: precision_at_1 value: 21.996 - type: precision_at_10 value: 5.065 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.392 - type: recall_at_1 value: 19.945 - type: recall_at_10 value: 43.62 - type: recall_at_100 value: 67.194 - type: recall_at_1000 value: 85.7 - type: recall_at_3 value: 32.15 - type: recall_at_5 value: 37.208999999999996 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 18.279 - type: map_at_10 value: 31.052999999999997 - type: map_at_100 value: 33.125 - type: map_at_1000 value: 33.306000000000004 - type: map_at_3 value: 26.208 - type: map_at_5 value: 28.857 - type: mrr_at_1 value: 42.671 - type: mrr_at_10 value: 54.557 - type: mrr_at_100 value: 55.142 - type: mrr_at_1000 value: 55.169000000000004 - type: mrr_at_3 value: 51.488 - type: mrr_at_5 value: 53.439 - type: ndcg_at_1 value: 42.671 - type: ndcg_at_10 value: 41.276 - type: ndcg_at_100 value: 48.376000000000005 - type: ndcg_at_1000 value: 51.318 - type: ndcg_at_3 value: 35.068 - type: ndcg_at_5 value: 37.242 - type: precision_at_1 value: 42.671 - type: precision_at_10 value: 12.638 - type: precision_at_100 value: 2.045 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 26.08 - type: precision_at_5 value: 19.805 - type: recall_at_1 value: 18.279 - type: recall_at_10 value: 46.946 - type: recall_at_100 value: 70.97200000000001 - type: recall_at_1000 value: 87.107 - type: recall_at_3 value: 31.147999999999996 - type: recall_at_5 value: 38.099 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.573 - type: map_at_10 value: 19.747 - type: map_at_100 value: 28.205000000000002 - type: map_at_1000 value: 29.831000000000003 - type: map_at_3 value: 14.109 - type: map_at_5 value: 16.448999999999998 - type: mrr_at_1 value: 71 - type: mrr_at_10 value: 77.68599999999999 - type: mrr_at_100 value: 77.995 - type: mrr_at_1000 value: 78.00200000000001 - type: mrr_at_3 value: 76.292 - type: mrr_at_5 value: 77.029 - type: ndcg_at_1 value: 59.12500000000001 - type: ndcg_at_10 value: 43.9 - type: ndcg_at_100 value: 47.863 - type: ndcg_at_1000 value: 54.848 - type: ndcg_at_3 value: 49.803999999999995 - type: ndcg_at_5 value: 46.317 - type: precision_at_1 value: 71 - type: precision_at_10 value: 34.4 - type: precision_at_100 value: 11.063 - type: precision_at_1000 value: 1.989 - type: precision_at_3 value: 52.333 - type: precision_at_5 value: 43.7 - type: recall_at_1 value: 8.573 - type: recall_at_10 value: 25.615 - type: recall_at_100 value: 53.385000000000005 - type: recall_at_1000 value: 75.46000000000001 - type: recall_at_3 value: 15.429 - type: recall_at_5 value: 19.357 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.989999999999995 - type: f1 value: 42.776314451497555 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.13499999999999 - type: map_at_10 value: 82.825 - type: map_at_100 value: 83.096 - type: map_at_1000 value: 83.111 - type: map_at_3 value: 81.748 - type: map_at_5 value: 82.446 - type: mrr_at_1 value: 79.553 - type: mrr_at_10 value: 86.654 - type: mrr_at_100 value: 86.774 - type: mrr_at_1000 value: 86.778 - type: mrr_at_3 value: 85.981 - type: mrr_at_5 value: 86.462 - type: ndcg_at_1 value: 79.553 - type: ndcg_at_10 value: 86.345 - type: ndcg_at_100 value: 87.32 - type: ndcg_at_1000 value: 87.58200000000001 - type: ndcg_at_3 value: 84.719 - type: ndcg_at_5 value: 85.677 - type: precision_at_1 value: 79.553 - type: precision_at_10 value: 10.402000000000001 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.413 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 74.13499999999999 - type: recall_at_10 value: 93.215 - type: recall_at_100 value: 97.083 - type: recall_at_1000 value: 98.732 - type: recall_at_3 value: 88.79 - type: recall_at_5 value: 91.259 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 18.298000000000002 - type: map_at_10 value: 29.901 - type: map_at_100 value: 31.528 - type: map_at_1000 value: 31.713 - type: map_at_3 value: 25.740000000000002 - type: map_at_5 value: 28.227999999999998 - type: mrr_at_1 value: 36.728 - type: mrr_at_10 value: 45.401 - type: mrr_at_100 value: 46.27 - type: mrr_at_1000 value: 46.315 - type: mrr_at_3 value: 42.978 - type: mrr_at_5 value: 44.29 - type: ndcg_at_1 value: 36.728 - type: ndcg_at_10 value: 37.456 - type: ndcg_at_100 value: 43.832 - type: ndcg_at_1000 value: 47 - type: ndcg_at_3 value: 33.694 - type: ndcg_at_5 value: 35.085 - type: precision_at_1 value: 36.728 - type: precision_at_10 value: 10.386 - type: precision_at_100 value: 1.701 - type: precision_at_1000 value: 0.22599999999999998 - type: precision_at_3 value: 22.479 - type: precision_at_5 value: 16.605 - type: recall_at_1 value: 18.298000000000002 - type: recall_at_10 value: 44.369 - type: recall_at_100 value: 68.098 - type: recall_at_1000 value: 87.21900000000001 - type: recall_at_3 value: 30.215999999999998 - type: recall_at_5 value: 36.861 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.568 - type: map_at_10 value: 65.061 - type: map_at_100 value: 65.896 - type: map_at_1000 value: 65.95100000000001 - type: map_at_3 value: 61.831 - type: map_at_5 value: 63.849000000000004 - type: mrr_at_1 value: 79.136 - type: mrr_at_10 value: 84.58200000000001 - type: mrr_at_100 value: 84.765 - type: mrr_at_1000 value: 84.772 - type: mrr_at_3 value: 83.684 - type: mrr_at_5 value: 84.223 - type: ndcg_at_1 value: 79.136 - type: ndcg_at_10 value: 72.622 - type: ndcg_at_100 value: 75.539 - type: ndcg_at_1000 value: 76.613 - type: ndcg_at_3 value: 68.065 - type: ndcg_at_5 value: 70.58 - type: precision_at_1 value: 79.136 - type: precision_at_10 value: 15.215 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 44.011 - type: precision_at_5 value: 28.388999999999996 - type: recall_at_1 value: 39.568 - type: recall_at_10 value: 76.077 - type: recall_at_100 value: 87.481 - type: recall_at_1000 value: 94.56400000000001 - type: recall_at_3 value: 66.01599999999999 - type: recall_at_5 value: 70.97200000000001 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.312 - type: ap value: 80.36296867333715 - type: f1 value: 85.26613311552218 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.363999999999997 - type: map_at_10 value: 35.711999999999996 - type: map_at_100 value: 36.876999999999995 - type: map_at_1000 value: 36.923 - type: map_at_3 value: 32.034 - type: map_at_5 value: 34.159 - type: mrr_at_1 value: 24.04 - type: mrr_at_10 value: 36.345 - type: mrr_at_100 value: 37.441 - type: mrr_at_1000 value: 37.480000000000004 - type: mrr_at_3 value: 32.713 - type: mrr_at_5 value: 34.824 - type: ndcg_at_1 value: 24.026 - type: ndcg_at_10 value: 42.531 - type: ndcg_at_100 value: 48.081 - type: ndcg_at_1000 value: 49.213 - type: ndcg_at_3 value: 35.044 - type: ndcg_at_5 value: 38.834 - type: precision_at_1 value: 24.026 - type: precision_at_10 value: 6.622999999999999 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.909 - type: precision_at_5 value: 10.871 - type: recall_at_1 value: 23.363999999999997 - type: recall_at_10 value: 63.426 - type: recall_at_100 value: 88.96300000000001 - type: recall_at_1000 value: 97.637 - type: recall_at_3 value: 43.095 - type: recall_at_5 value: 52.178000000000004 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.0095759233926 - type: f1 value: 92.78387794667408 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.0296397628819 - type: f1 value: 58.45699589820874 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.45662407531944 - type: f1 value: 71.42364781421813 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.07800941492937 - type: f1 value: 77.22799045640845 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.531234379250606 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.941490381193802 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.3115090856725 - type: mrr value: 31.290667638675757 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.465 - type: map_at_10 value: 13.03 - type: map_at_100 value: 16.057 - type: map_at_1000 value: 17.49 - type: map_at_3 value: 9.553 - type: map_at_5 value: 11.204 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.269 - type: mrr_at_100 value: 53.72 - type: mrr_at_1000 value: 53.761 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 52.461 - type: ndcg_at_1 value: 42.26 - type: ndcg_at_10 value: 34.673 - type: ndcg_at_100 value: 30.759999999999998 - type: ndcg_at_1000 value: 39.728 - type: ndcg_at_3 value: 40.349000000000004 - type: ndcg_at_5 value: 37.915 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.789 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.596000000000004 - type: precision_at_5 value: 33.251 - type: recall_at_1 value: 5.465 - type: recall_at_10 value: 17.148 - type: recall_at_100 value: 29.768 - type: recall_at_1000 value: 62.239 - type: recall_at_3 value: 10.577 - type: recall_at_5 value: 13.315 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 37.008 - type: map_at_10 value: 52.467 - type: map_at_100 value: 53.342999999999996 - type: map_at_1000 value: 53.366 - type: map_at_3 value: 48.412 - type: map_at_5 value: 50.875 - type: mrr_at_1 value: 41.541 - type: mrr_at_10 value: 54.967 - type: mrr_at_100 value: 55.611 - type: mrr_at_1000 value: 55.627 - type: mrr_at_3 value: 51.824999999999996 - type: mrr_at_5 value: 53.763000000000005 - type: ndcg_at_1 value: 41.541 - type: ndcg_at_10 value: 59.724999999999994 - type: ndcg_at_100 value: 63.38700000000001 - type: ndcg_at_1000 value: 63.883 - type: ndcg_at_3 value: 52.331 - type: ndcg_at_5 value: 56.327000000000005 - type: precision_at_1 value: 41.541 - type: precision_at_10 value: 9.447 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.262 - type: precision_at_5 value: 16.314999999999998 - type: recall_at_1 value: 37.008 - type: recall_at_10 value: 79.145 - type: recall_at_100 value: 94.986 - type: recall_at_1000 value: 98.607 - type: recall_at_3 value: 60.277 - type: recall_at_5 value: 69.407 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.402 - type: map_at_10 value: 84.181 - type: map_at_100 value: 84.796 - type: map_at_1000 value: 84.81400000000001 - type: map_at_3 value: 81.209 - type: map_at_5 value: 83.085 - type: mrr_at_1 value: 81.02000000000001 - type: mrr_at_10 value: 87.263 - type: mrr_at_100 value: 87.36 - type: mrr_at_1000 value: 87.36 - type: mrr_at_3 value: 86.235 - type: mrr_at_5 value: 86.945 - type: ndcg_at_1 value: 81.01 - type: ndcg_at_10 value: 87.99900000000001 - type: ndcg_at_100 value: 89.217 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 85.053 - type: ndcg_at_5 value: 86.703 - type: precision_at_1 value: 81.01 - type: precision_at_10 value: 13.336 - type: precision_at_100 value: 1.52 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 24.44 - type: recall_at_1 value: 70.402 - type: recall_at_10 value: 95.214 - type: recall_at_100 value: 99.438 - type: recall_at_1000 value: 99.928 - type: recall_at_3 value: 86.75699999999999 - type: recall_at_5 value: 91.44099999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.51721502758904 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 61.054808572333016 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.578 - type: map_at_10 value: 11.036999999999999 - type: map_at_100 value: 12.879999999999999 - type: map_at_1000 value: 13.150999999999998 - type: map_at_3 value: 8.133 - type: map_at_5 value: 9.559 - type: mrr_at_1 value: 22.6 - type: mrr_at_10 value: 32.68 - type: mrr_at_100 value: 33.789 - type: mrr_at_1000 value: 33.854 - type: mrr_at_3 value: 29.7 - type: mrr_at_5 value: 31.480000000000004 - type: ndcg_at_1 value: 22.6 - type: ndcg_at_10 value: 18.616 - type: ndcg_at_100 value: 25.883 - type: ndcg_at_1000 value: 30.944 - type: ndcg_at_3 value: 18.136 - type: ndcg_at_5 value: 15.625 - type: precision_at_1 value: 22.6 - type: precision_at_10 value: 9.48 - type: precision_at_100 value: 1.991 - type: precision_at_1000 value: 0.321 - type: precision_at_3 value: 16.8 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.578 - type: recall_at_10 value: 19.213 - type: recall_at_100 value: 40.397 - type: recall_at_1000 value: 65.2 - type: recall_at_3 value: 10.208 - type: recall_at_5 value: 13.718 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.44288351714071 - type: cos_sim_spearman value: 79.37995604564952 - type: euclidean_pearson value: 81.1078874670718 - type: euclidean_spearman value: 79.37995905980499 - type: manhattan_pearson value: 81.03697527288986 - type: manhattan_spearman value: 79.33490235296236 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.95557650436523 - type: cos_sim_spearman value: 78.5190672399868 - type: euclidean_pearson value: 81.58064025904707 - type: euclidean_spearman value: 78.5190672399868 - type: manhattan_pearson value: 81.52857930619889 - type: manhattan_spearman value: 78.50421361308034 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.79128416228737 - type: cos_sim_spearman value: 86.05402451477147 - type: euclidean_pearson value: 85.46280267054289 - type: euclidean_spearman value: 86.05402451477147 - type: manhattan_pearson value: 85.46278563858236 - type: manhattan_spearman value: 86.08079590861004 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.20623089568763 - type: cos_sim_spearman value: 81.53786907061009 - type: euclidean_pearson value: 82.82272250091494 - type: euclidean_spearman value: 81.53786907061009 - type: manhattan_pearson value: 82.78850494027013 - type: manhattan_spearman value: 81.5135618083407 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.46366618397936 - type: cos_sim_spearman value: 86.96566013336908 - type: euclidean_pearson value: 86.62651697548931 - type: euclidean_spearman value: 86.96565526364454 - type: manhattan_pearson value: 86.58812160258009 - type: manhattan_spearman value: 86.9336484321288 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.51858358641559 - type: cos_sim_spearman value: 84.7652527954999 - type: euclidean_pearson value: 84.23914783766861 - type: euclidean_spearman value: 84.7652527954999 - type: manhattan_pearson value: 84.22749648503171 - type: manhattan_spearman value: 84.74527996746386 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.28026563313065 - type: cos_sim_spearman value: 87.46928143824915 - type: euclidean_pearson value: 88.30558762000372 - type: euclidean_spearman value: 87.46928143824915 - type: manhattan_pearson value: 88.10513330809331 - type: manhattan_spearman value: 87.21069787834173 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.376497134587375 - type: cos_sim_spearman value: 65.0159550112516 - type: euclidean_pearson value: 65.64572120879598 - type: euclidean_spearman value: 65.0159550112516 - type: manhattan_pearson value: 65.88143604989976 - type: manhattan_spearman value: 65.17547297222434 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.22876368947644 - type: cos_sim_spearman value: 85.46935577445318 - type: euclidean_pearson value: 85.32830231392005 - type: euclidean_spearman value: 85.46935577445318 - type: manhattan_pearson value: 85.30353211758495 - type: manhattan_spearman value: 85.42821085956945 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 80.60986667767133 - type: mrr value: 94.29432314236236 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 54.528 - type: map_at_10 value: 65.187 - type: map_at_100 value: 65.62599999999999 - type: map_at_1000 value: 65.657 - type: map_at_3 value: 62.352 - type: map_at_5 value: 64.025 - type: mrr_at_1 value: 57.333 - type: mrr_at_10 value: 66.577 - type: mrr_at_100 value: 66.88 - type: mrr_at_1000 value: 66.908 - type: mrr_at_3 value: 64.556 - type: mrr_at_5 value: 65.739 - type: ndcg_at_1 value: 57.333 - type: ndcg_at_10 value: 70.275 - type: ndcg_at_100 value: 72.136 - type: ndcg_at_1000 value: 72.963 - type: ndcg_at_3 value: 65.414 - type: ndcg_at_5 value: 67.831 - type: precision_at_1 value: 57.333 - type: precision_at_10 value: 9.5 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.778000000000002 - type: precision_at_5 value: 17.2 - type: recall_at_1 value: 54.528 - type: recall_at_10 value: 84.356 - type: recall_at_100 value: 92.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 71.283 - type: recall_at_5 value: 77.14999999999999 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.74158415841585 - type: cos_sim_ap value: 92.90048959850317 - type: cos_sim_f1 value: 86.35650810245687 - type: cos_sim_precision value: 90.4709748083242 - type: cos_sim_recall value: 82.6 - type: dot_accuracy value: 99.74158415841585 - type: dot_ap value: 92.90048959850317 - type: dot_f1 value: 86.35650810245687 - type: dot_precision value: 90.4709748083242 - type: dot_recall value: 82.6 - type: euclidean_accuracy value: 99.74158415841585 - type: euclidean_ap value: 92.90048959850317 - type: euclidean_f1 value: 86.35650810245687 - type: euclidean_precision value: 90.4709748083242 - type: euclidean_recall value: 82.6 - type: manhattan_accuracy value: 99.74158415841585 - type: manhattan_ap value: 92.87344692947894 - type: manhattan_f1 value: 86.38497652582159 - type: manhattan_precision value: 90.29443838604145 - type: manhattan_recall value: 82.8 - type: max_accuracy value: 99.74158415841585 - type: max_ap value: 92.90048959850317 - type: max_f1 value: 86.38497652582159 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 63.191648770424216 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.02944668730218 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.466386167525265 - type: mrr value: 51.19071492233257 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.198022505886435 - type: cos_sim_spearman value: 30.40170257939193 - type: dot_pearson value: 30.198015316402614 - type: dot_spearman value: 30.40170257939193 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.242 - type: map_at_10 value: 2.17 - type: map_at_100 value: 12.221 - type: map_at_1000 value: 28.63 - type: map_at_3 value: 0.728 - type: map_at_5 value: 1.185 - type: mrr_at_1 value: 94 - type: mrr_at_10 value: 97 - type: mrr_at_100 value: 97 - type: mrr_at_1000 value: 97 - type: mrr_at_3 value: 97 - type: mrr_at_5 value: 97 - type: ndcg_at_1 value: 89 - type: ndcg_at_10 value: 82.30499999999999 - type: ndcg_at_100 value: 61.839999999999996 - type: ndcg_at_1000 value: 53.381 - type: ndcg_at_3 value: 88.877 - type: ndcg_at_5 value: 86.05199999999999 - type: precision_at_1 value: 94 - type: precision_at_10 value: 87 - type: precision_at_100 value: 63.38 - type: precision_at_1000 value: 23.498 - type: precision_at_3 value: 94 - type: precision_at_5 value: 92 - type: recall_at_1 value: 0.242 - type: recall_at_10 value: 2.302 - type: recall_at_100 value: 14.979000000000001 - type: recall_at_1000 value: 49.638 - type: recall_at_3 value: 0.753 - type: recall_at_5 value: 1.226 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.006 - type: map_at_10 value: 11.805 - type: map_at_100 value: 18.146 - type: map_at_1000 value: 19.788 - type: map_at_3 value: 5.914 - type: map_at_5 value: 8.801 - type: mrr_at_1 value: 40.816 - type: mrr_at_10 value: 56.36600000000001 - type: mrr_at_100 value: 56.721999999999994 - type: mrr_at_1000 value: 56.721999999999994 - type: mrr_at_3 value: 52.041000000000004 - type: mrr_at_5 value: 54.796 - type: ndcg_at_1 value: 37.755 - type: ndcg_at_10 value: 29.863 - type: ndcg_at_100 value: 39.571 - type: ndcg_at_1000 value: 51.385999999999996 - type: ndcg_at_3 value: 32.578 - type: ndcg_at_5 value: 32.351 - type: precision_at_1 value: 40.816 - type: precision_at_10 value: 26.531 - type: precision_at_100 value: 7.796 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 32.653 - type: precision_at_5 value: 33.061 - type: recall_at_1 value: 3.006 - type: recall_at_10 value: 18.738 - type: recall_at_100 value: 48.058 - type: recall_at_1000 value: 83.41300000000001 - type: recall_at_3 value: 7.166 - type: recall_at_5 value: 12.102 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.4178 - type: ap value: 14.648781342150446 - type: f1 value: 55.07299194946378 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.919637804187886 - type: f1 value: 61.24122013967399 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.207896583685695 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.23114978840078 - type: cos_sim_ap value: 74.26624727825818 - type: cos_sim_f1 value: 68.72377190817083 - type: cos_sim_precision value: 64.56400742115028 - type: cos_sim_recall value: 73.45646437994723 - type: dot_accuracy value: 86.23114978840078 - type: dot_ap value: 74.26624032659652 - type: dot_f1 value: 68.72377190817083 - type: dot_precision value: 64.56400742115028 - type: dot_recall value: 73.45646437994723 - type: euclidean_accuracy value: 86.23114978840078 - type: euclidean_ap value: 74.26624714480556 - type: euclidean_f1 value: 68.72377190817083 - type: euclidean_precision value: 64.56400742115028 - type: euclidean_recall value: 73.45646437994723 - type: manhattan_accuracy value: 86.16558383501221 - type: manhattan_ap value: 74.2091943976357 - type: manhattan_f1 value: 68.64221520524654 - type: manhattan_precision value: 63.59135913591359 - type: manhattan_recall value: 74.5646437994723 - type: max_accuracy value: 86.23114978840078 - type: max_ap value: 74.26624727825818 - type: max_f1 value: 68.72377190817083 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.3681841114604 - type: cos_sim_ap value: 86.65166387498546 - type: cos_sim_f1 value: 79.02581944698774 - type: cos_sim_precision value: 75.35796605434099 - type: cos_sim_recall value: 83.06898675700647 - type: dot_accuracy value: 89.3681841114604 - type: dot_ap value: 86.65166019802056 - type: dot_f1 value: 79.02581944698774 - type: dot_precision value: 75.35796605434099 - type: dot_recall value: 83.06898675700647 - type: euclidean_accuracy value: 89.3681841114604 - type: euclidean_ap value: 86.65166462876266 - type: euclidean_f1 value: 79.02581944698774 - type: euclidean_precision value: 75.35796605434099 - type: euclidean_recall value: 83.06898675700647 - type: manhattan_accuracy value: 89.36624364497226 - type: manhattan_ap value: 86.65076471274106 - type: manhattan_f1 value: 79.07408783532733 - type: manhattan_precision value: 76.41102972856527 - type: manhattan_recall value: 81.92947336002464 - type: max_accuracy value: 89.3681841114604 - type: max_ap value: 86.65166462876266 - type: max_f1 value: 79.07408783532733 --- # nesall/nomic-embed-text-v1.5-Q6_K-GGUF This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nesall/nomic-embed-text-v1.5-Q6_K-GGUF --hf-file nomic-embed-text-v1.5-q6_k.gguf -c 2048 ```

ultratopaz/1321983

ultratopaz

2025-09-05T19:53:38Z

0

null

[ "region:us" ]

null

2025-09-05T19:53:38Z

[View on Civ Archive](https://civarchive.com/models/1258863?modelVersionId=1419421)

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q4_1-SPECIAL_SPLIT

Thireus

2025-09-05T19:53:17Z

4

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-26T04:08:44Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

ultratopaz/1272206

ultratopaz

2025-09-05T19:53:13Z

0

null

[ "region:us" ]

null

2025-09-05T19:53:13Z

[View on Civ Archive](https://civarchive.com/models/1145757?modelVersionId=1369242)

seraphimzzzz/1269773

seraphimzzzz

2025-09-05T19:53:01Z

0

null

[ "region:us" ]

null

2025-09-05T19:53:01Z

[View on Civ Archive](https://civarchive.com/models/1211769?modelVersionId=1364902)

ultratopaz/1248399

ultratopaz

2025-09-05T19:52:49Z

0

null

[ "region:us" ]

null

2025-09-05T19:52:49Z

[View on Civ Archive](https://civarchive.com/models/1193362?modelVersionId=1343608)

amethyst9/1284777

amethyst9

2025-09-05T19:52:32Z

0

null

[ "region:us" ]

null

2025-09-05T19:52:31Z

[View on Civ Archive](https://civarchive.com/models/1225756?modelVersionId=1381044)

Viktor-01/blockassist-bc-leaping_humming_finch_1757099760

Viktor-01

2025-09-05T19:51:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "leaping humming finch", "arxiv:2504.07091", "region:us" ]

null

2025-09-05T19:51:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - leaping humming finch --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

seraphimzzzz/1256476

seraphimzzzz

2025-09-05T19:51:40Z

0

null

[ "region:us" ]

null

2025-09-05T19:51:40Z

[View on Civ Archive](https://civarchive.com/models/1200481?modelVersionId=1351765)

ultratopaz/1271408

ultratopaz

2025-09-05T19:51:28Z

0

null

[ "region:us" ]

null

2025-09-05T19:51:28Z

[View on Civ Archive](https://civarchive.com/models/1212847?modelVersionId=1366152)

amethyst9/1293545

amethyst9

2025-09-05T19:51:14Z

0

null

[ "region:us" ]

null

2025-09-05T19:51:14Z

[View on Civ Archive](https://civarchive.com/models/1233536?modelVersionId=1390039)

seraphimzzzz/1283580

seraphimzzzz

2025-09-05T19:51:02Z

0

null

[ "region:us" ]

null

2025-09-05T19:51:02Z

[View on Civ Archive](https://civarchive.com/models/1223637?modelVersionId=1378618)

seraphimzzzz/1253194

seraphimzzzz

2025-09-05T19:50:51Z

0

null

[ "region:us" ]

null

2025-09-05T19:50:51Z

[View on Civ Archive](https://civarchive.com/models/1197280?modelVersionId=1348112)

Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-Q3_K_R4-SPECIAL_SPLIT

Thireus

2025-09-05T19:49:51Z

0

null

[ "gguf", "arxiv:2505.23786", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-07-25T18:54:37Z

--- license: mit --- # Qwen3-235B-A22B-Instruct-2507 ## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-235B-A22B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about? This repository provides **GGUF-quantized tensors** for the Qwen3-235B-A22B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly. - 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite - 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples - 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb - 📂 Browse available quant shards: https://huggingface.co/Thireus/collections *tl;dr: Expand the details section below* <details> ``` cd ~ # Make sure to install all ik_llama.cpp compilation dependencies... apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx # Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases git clone https://github.com/Thireus/ik_llama.cpp cd ik_llama.cpp git pull # Build ik_llama.cpp cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048 cmake --build build --config Release -j16 cd .. # Obtain Thireus' GGUF-Tool-Suite git clone https://github.com/Thireus/GGUF-Tool-Suite # Download model quant mix from recipe file: cd GGUF-Tool-Suite rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model mkdir -p kitchen && cd kitchen ../quant_downloader.sh ../recipe_examples/ik_harmonized_recipes/DeepSeek-R1-0528.ROOT-2.7921bpw-3.4451ppl.218GB-GGUF_14GB-GPU_204GB-CPU.90e3c2f_6f5170d.recipe # Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples # Launch ik_llama's llama-cli: ulimit -n 9999 # Lifts "too many open files" limitation on Linux ~/ik_llama.cpp/build/bin/llama-cli \ -m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \ -mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \ -ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \ -ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \ -ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \ --main-gpu 0 \ -p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n' ``` </details> --- ## ❓ Why does this Tool Suite exist? 1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`. 2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity. 3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results! --- ## 📊 How does it compare to other GGUFs? Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw): ![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/DeepSeek-R1-0528.svg) > _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._ More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs *All PPL values are computed with the parameters `-ctk f16 -c 512 -b 4096 -ub 4096`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 4096 -ub 4096` increases the PPL, while increasing them decreases the PPL.* --- ## 🚀 How do I get started? Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections: 1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile. - Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases 2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe. - Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples 3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`. 4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity. --- ## ✅ Supported Models Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`. --- ## 🤷‍♂️ Will I release baked dynamic quant GGUFs? No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s. Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`. Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`. --- ## 📦 What’s in this repository? - **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard. - **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc. - **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection. - **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits. --- ## 💡 Pro Tips You can easily download the BF16 model version to quantize your own shards: ``` mkdir kitchen echo '.*=bf16' > kitchen/bf16.recipe cd kitchen ../quant_downloader.sh bf16.recipe ``` Enjoy optimized quantization! 🎉

ultratopaz/1261009

ultratopaz

2025-09-05T19:49:46Z

0

null

[ "region:us" ]

null

2025-09-05T19:49:46Z

[View on Civ Archive](https://civarchive.com/models/1204451?modelVersionId=1356335)

crystalline7/1292031

crystalline7

2025-09-05T19:49:32Z

0

null

[ "region:us" ]

null

2025-09-05T19:49:32Z

[View on Civ Archive](https://civarchive.com/models/1231282?modelVersionId=1387424)

amethyst9/1236646

amethyst9

2025-09-05T19:49:08Z

0

null

[ "region:us" ]

null

2025-09-05T19:49:06Z

[View on Civ Archive](https://civarchive.com/models/1183550?modelVersionId=1332070)

ultratopaz/1289747

ultratopaz

2025-09-05T19:48:29Z

0

null

[ "region:us" ]

null

2025-09-05T19:48:30Z

[View on Civ Archive](https://civarchive.com/models/1229799?modelVersionId=1385741)

seraphimzzzz/1386320

seraphimzzzz

2025-09-05T19:48:05Z

0

null

[ "region:us" ]

null

2025-09-05T19:48:06Z

[View on Civ Archive](https://civarchive.com/models/1315091?modelVersionId=1484608)

crystalline7/1202072

crystalline7

2025-09-05T19:47:38Z

0

null

[ "region:us" ]

null

2025-09-05T19:47:35Z

[View on Civ Archive](https://civarchive.com/models/1150650?modelVersionId=1294177)

seraphimzzzz/1267523

seraphimzzzz

2025-09-05T19:47:23Z

0

null

[ "region:us" ]

null

2025-09-05T19:47:23Z

[View on Civ Archive](https://civarchive.com/models/1209317?modelVersionId=1362024)

mamersfo/Llama-3.2-3B-Instruct-sft-hr

mamersfo

2025-09-05T19:46:49Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-09-05T19:46:39Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** mamersfo - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

amethyst9/1223596

amethyst9

2025-09-05T19:46:47Z

0

null

[ "region:us" ]

null

2025-09-05T19:46:47Z

[View on Civ Archive](https://civarchive.com/models/1171788?modelVersionId=1318414)

seraphimzzzz/1301630

seraphimzzzz

2025-09-05T19:46:36Z

0

null

[ "region:us" ]

null

2025-09-05T19:46:36Z

[View on Civ Archive](https://civarchive.com/models/1240588?modelVersionId=1398212)