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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* +
+ +``` +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_llama.cpp_recipes/DeepSeek-R1-0528.THIREUS-1.9364bpw-4.3533ppl.151GB-GGUF_11GB-GPU_140GB-CPU.3c88ec6_9fd615d.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｜>\n' +``` + +
+ +--- + +## ❓ 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 + +--- + +## 🚀 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! 🎉 diff --git a/tensors.map b/tensors.map new file mode 100644 index 0000000000000000000000000000000000000000..77677a7295f5383be232b089f33cead5d43e8906 --- /dev/null +++ b/tensors.map @@ -0,0 +1,398 @@ +Qwen3-4B-Instruct-2507-THIREUS-Q6_K_R4-SPECIAL_TENSOR-00002-of-00399.gguf:f27b047fb83cd6c7a11cc38398fffd418a467957c2f8377891a66cac06c91f2a:token_embd.weight:shape=(2560, 151936):dtype=q6_k:elements=388956160:bytes=319065600 +Qwen3-4B-Instruct-2507-THIREUS-Q6_K_R4-SPECIAL_TENSOR-00003-of-00399.gguf:14ec32fffcaae4bc2f06b3d1cce1e5f9f926f1ab54a2c9a5a9d2731bdeabfa42:blk.0.attn_norm.weight:shape=(2560,):dtype=f32:elements=2560:bytes=10240 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