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--- |
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quantized_by: bartowski |
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pipeline_tag: text-generation |
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language: |
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- zh |
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- en |
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license: mit |
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base_model: THUDM/GLM-Z1-Rumination-32B-0414 |
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base_model_relation: quantized |
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--- |
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## Llamacpp imatrix Quantizations of GLM-Z1-Rumination-32B-0414 by THUDM |
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5228">b5228</a> for quantization. |
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Original model: https://huggingface.co/THUDM/GLM-Z1-Rumination-32B-0414 |
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All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) |
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Run them in [LM Studio](https://lmstudio.ai/) |
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Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project |
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## Prompt format |
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``` |
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[gMASK]<sop> |
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<|system|> |
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你是一个专业的深度研究助手，通过提供的工具与模拟浏览器交互，来帮助用户完成深度信息调研和报告撰写任务。今年是 2025 年。 |
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<核心要求> |
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- 首先分解用户请求，得到包含多个子要求的列表 |
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- 制定初始研究计划 |
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- 进行多轮迭代搜索和页面浏览（at least 10 function calls）： |
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* 根据已获得的信息调整研究计划和关键词 |
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* 打开页面阅读，从发现的内容中识别新的关键概念/名词 |
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* 从搜索结果中提取新的关键词继续搜索 |
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* 访问并仔细阅读相关页面，识别新的关键概念/名词 |
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<重要配置> |
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- 采用语言 |
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* 搜索关键词：英文 |
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* 思考：英文 |
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<可调用的工具列表> |
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[{"name": "search", "description": "Execute a search query and return search results. Use this function when you need to find information about a specific topic.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query string, use English words unless it is a proper name in Chinese"}}, "required": ["query"], "additionalProperties": false}}, {"name": "click", "description": "Click a link in the search results and navigate to the corresponding page. Use this function when you need to view detailed content of a specific search result.", "parameters": {"type": "object", "properties": {"link_id": {"type": "integer", "description": "The link ID to click (from the sequence number in search results)"}}, "required": ["link_id"], "additionalProperties": false}}, {"name": "open", "description": "Open a specific website. Get content from any website with its URL.", "parameters": {"type": "object", "properties": {"url": {"type": "string", "description": "The target website URL or domain"}}, "required": ["url"], "additionalProperties": false}}, {"name": "finish", "description": "Finish the task. Use this function when you have found the information you need.", "parameters": {"type": "object", "properties": {}, "additionalProperties": false}}]<|user|> |
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{prompt}<|assistant|> |
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``` |
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## What's new: |
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Fix tokenizer |
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## Download a file (not the whole branch) from below: |
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| Filename | Quant type | File Size | Split | Description | |
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| -------- | ---------- | --------- | ----- | ----------- | |
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| [GLM-Z1-Rumination-32B-0414-bf16.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/tree/main/THUDM_GLM-Z1-Rumination-32B-0414-bf16) | bf16 | 66.30GB | true | Full BF16 weights. | |
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| [GLM-Z1-Rumination-32B-0414-Q8_0.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q8_0.gguf) | Q8_0 | 35.23GB | false | Extremely high quality, generally unneeded but max available quant. | |
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| [GLM-Z1-Rumination-32B-0414-Q6_K_L.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q6_K_L.gguf) | Q6_K_L | 27.65GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q6_K.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q6_K.gguf) | Q6_K | 27.20GB | false | Very high quality, near perfect, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q5_K_L.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q5_K_L.gguf) | Q5_K_L | 24.09GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q5_K_M.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q5_K_M.gguf) | Q5_K_M | 23.51GB | false | High quality, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q5_K_S.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q5_K_S.gguf) | Q5_K_S | 22.92GB | false | High quality, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q4_1.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q4_1.gguf) | Q4_1 | 20.91GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. | |
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| [GLM-Z1-Rumination-32B-0414-Q4_K_L.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q4_K_L.gguf) | Q4_K_L | 20.73GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q4_K_M.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q4_K_M.gguf) | Q4_K_M | 20.04GB | false | Good quality, default size for most use cases, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q4_K_S.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q4_K_S.gguf) | Q4_K_S | 19.02GB | false | Slightly lower quality with more space savings, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q4_0.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q4_0.gguf) | Q4_0 | 18.96GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. | |
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| [GLM-Z1-Rumination-32B-0414-IQ4_NL.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ4_NL.gguf) | IQ4_NL | 18.94GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. | |
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| [GLM-Z1-Rumination-32B-0414-Q3_K_XL.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q3_K_XL.gguf) | Q3_K_XL | 18.35GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | |
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| [GLM-Z1-Rumination-32B-0414-IQ4_XS.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ4_XS.gguf) | IQ4_XS | 17.95GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | |
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| [GLM-Z1-Rumination-32B-0414-Q3_K_L.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q3_K_L.gguf) | Q3_K_L | 17.54GB | false | Lower quality but usable, good for low RAM availability. | |
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| [GLM-Z1-Rumination-32B-0414-Q3_K_M.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q3_K_M.gguf) | Q3_K_M | 16.18GB | false | Low quality. | |
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| [GLM-Z1-Rumination-32B-0414-IQ3_M.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ3_M.gguf) | IQ3_M | 15.11GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | |
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| [GLM-Z1-Rumination-32B-0414-Q3_K_S.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q3_K_S.gguf) | Q3_K_S | 14.62GB | false | Low quality, not recommended. | |
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| [GLM-Z1-Rumination-32B-0414-IQ3_XS.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ3_XS.gguf) | IQ3_XS | 13.93GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. | |
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| [GLM-Z1-Rumination-32B-0414-Q2_K_L.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q2_K_L.gguf) | Q2_K_L | 13.46GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. | |
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| [GLM-Z1-Rumination-32B-0414-IQ3_XXS.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ3_XXS.gguf) | IQ3_XXS | 13.04GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. | |
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| [GLM-Z1-Rumination-32B-0414-Q2_K.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-Q2_K.gguf) | Q2_K | 12.55GB | false | Very low quality but surprisingly usable. | |
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| [GLM-Z1-Rumination-32B-0414-IQ2_M.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ2_M.gguf) | IQ2_M | 11.53GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. | |
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| [GLM-Z1-Rumination-32B-0414-IQ2_S.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ2_S.gguf) | IQ2_S | 10.67GB | false | Low quality, uses SOTA techniques to be usable. | |
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| [GLM-Z1-Rumination-32B-0414-IQ2_XS.gguf](https://huggingface.co/bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF/blob/main/THUDM_GLM-Z1-Rumination-32B-0414-IQ2_XS.gguf) | IQ2_XS | 10.15GB | false | Low quality, uses SOTA techniques to be usable. | |
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## Embed/output weights |
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. |
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## Downloading using huggingface-cli |
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<details> |
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<summary>Click to view download instructions</summary> |
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First, make sure you have hugginface-cli installed: |
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``` |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, you can target the specific file you want: |
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``` |
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huggingface-cli download bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF --include "THUDM_GLM-Z1-Rumination-32B-0414-Q4_K_M.gguf" --local-dir ./ |
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``` |
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: |
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``` |
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huggingface-cli download bartowski/THUDM_GLM-Z1-Rumination-32B-0414-GGUF --include "THUDM_GLM-Z1-Rumination-32B-0414-Q8_0/*" --local-dir ./ |
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``` |
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You can either specify a new local-dir (THUDM_GLM-Z1-Rumination-32B-0414-Q8_0) or download them all in place (./) |
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</details> |
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## ARM/AVX information |
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Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. |
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Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. |
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As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. |
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Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. |
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<details> |
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<summary>Click to view Q4_0_X_X information (deprecated</summary> |
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I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. |
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<details> |
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<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> |
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| model | size | params | backend | threads | test | t/s | % (vs Q4_0) | |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% | |
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Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation |
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</details> |
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</details> |
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## Which file should I choose? |
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<details> |
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<summary>Click here for details</summary> |
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A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) |
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The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. |
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If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. |
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If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. |
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Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. |
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If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. |
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If you want to get more into the weeds, you can check out this extremely useful feature chart: |
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[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) |
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But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. |
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These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. |
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</details> |
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## Credits |
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Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. |
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Thank you ZeroWw for the inspiration to experiment with embed/output. |
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Thank you to LM Studio for sponsoring my work. |
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
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