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Model: bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF Source: Original Platform
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---
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quantized_by: bartowski
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pipeline_tag: text-generation
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base_model_relation: quantized
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base_model: HelpingAI/Dhanishtha-2.0-preview-0825
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---
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## Llamacpp imatrix Quantizations of Dhanishtha-2.0-preview-0825 by HelpingAI
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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/b6014">b6014</a> for quantization.
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Original model: https://huggingface.co/HelpingAI/Dhanishtha-2.0-preview-0825
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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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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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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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| [Dhanishtha-2.0-preview-0825-bf16.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-bf16.gguf) | bf16 | 29.54GB | false | Full BF16 weights. |
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| [Dhanishtha-2.0-preview-0825-Q8_0.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q8_0.gguf) | Q8_0 | 15.70GB | false | Extremely high quality, generally unneeded but max available quant. |
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| [Dhanishtha-2.0-preview-0825-Q6_K_L.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q6_K_L.gguf) | Q6_K_L | 12.50GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
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| [Dhanishtha-2.0-preview-0825-Q6_K.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q6_K.gguf) | Q6_K | 12.12GB | false | Very high quality, near perfect, *recommended*. |
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| [Dhanishtha-2.0-preview-0825-Q5_K_L.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q5_K_L.gguf) | Q5_K_L | 10.99GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
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||||||
|
| [Dhanishtha-2.0-preview-0825-Q5_K_M.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q5_K_M.gguf) | Q5_K_M | 10.51GB | false | High quality, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q5_K_S.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q5_K_S.gguf) | Q5_K_S | 10.26GB | false | High quality, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q4_K_L.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q4_K_L.gguf) | Q4_K_L | 9.58GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q4_1.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q4_1.gguf) | Q4_1 | 9.39GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q4_K_M.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q4_K_M.gguf) | Q4_K_M | 9.00GB | false | Good quality, default size for most use cases, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q3_K_XL.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q3_K_XL.gguf) | Q3_K_XL | 8.58GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q4_K_S.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q4_K_S.gguf) | Q4_K_S | 8.57GB | false | Slightly lower quality with more space savings, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q4_0.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q4_0.gguf) | Q4_0 | 8.54GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ4_NL.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ4_NL.gguf) | IQ4_NL | 8.54GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ4_XS.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ4_XS.gguf) | IQ4_XS | 8.11GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q3_K_L.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q3_K_L.gguf) | Q3_K_L | 7.90GB | false | Lower quality but usable, good for low RAM availability. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q3_K_M.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q3_K_M.gguf) | Q3_K_M | 7.32GB | false | Low quality. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ3_M.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ3_M.gguf) | IQ3_M | 6.88GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q3_K_S.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q3_K_S.gguf) | Q3_K_S | 6.66GB | false | Low quality, not recommended. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q2_K_L.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q2_K_L.gguf) | Q2_K_L | 6.51GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ3_XS.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ3_XS.gguf) | IQ3_XS | 6.38GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ3_XXS.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ3_XXS.gguf) | IQ3_XXS | 5.94GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-Q2_K.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-Q2_K.gguf) | Q2_K | 5.75GB | false | Very low quality but surprisingly usable. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ2_M.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ2_M.gguf) | IQ2_M | 5.32GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
|
||||||
|
| [Dhanishtha-2.0-preview-0825-IQ2_S.gguf](https://huggingface.co/bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF/blob/main/HelpingAI_Dhanishtha-2.0-preview-0825-IQ2_S.gguf) | IQ2_S | 4.96GB | false | Low quality, uses SOTA techniques to be usable. |
|
||||||
|
|
||||||
|
## Embed/output weights
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
## Downloading using huggingface-cli
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>Click to view download instructions</summary>
|
||||||
|
|
||||||
|
First, make sure you have hugginface-cli installed:
|
||||||
|
|
||||||
|
```
|
||||||
|
pip install -U "huggingface_hub[cli]"
|
||||||
|
```
|
||||||
|
|
||||||
|
Then, you can target the specific file you want:
|
||||||
|
|
||||||
|
```
|
||||||
|
huggingface-cli download bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF --include "HelpingAI_Dhanishtha-2.0-preview-0825-Q4_K_M.gguf" --local-dir ./
|
||||||
|
```
|
||||||
|
|
||||||
|
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:
|
||||||
|
|
||||||
|
```
|
||||||
|
huggingface-cli download bartowski/HelpingAI_Dhanishtha-2.0-preview-0825-GGUF --include "HelpingAI_Dhanishtha-2.0-preview-0825-Q8_0/*" --local-dir ./
|
||||||
|
```
|
||||||
|
|
||||||
|
You can either specify a new local-dir (HelpingAI_Dhanishtha-2.0-preview-0825-Q8_0) or download them all in place (./)
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## ARM/AVX information
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>Click to view Q4_0_X_X information (deprecated</summary>
|
||||||
|
|
||||||
|
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
|
||||||
|
|
||||||
|
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
|
||||||
|
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
|
||||||
|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
|
||||||
|
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
|
||||||
|
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
|
||||||
|
|
||||||
|
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## Which file should I choose?
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>Click here for details</summary>
|
||||||
|
|
||||||
|
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
If you want to get more into the weeds, you can check out this extremely useful feature chart:
|
||||||
|
|
||||||
|
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## Credits
|
||||||
|
|
||||||
|
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
|
||||||
|
|
||||||
|
Thank you ZeroWw for the inspiration to experiment with embed/output.
|
||||||
|
|
||||||
|
Thank you to LM Studio for sponsoring my work.
|
||||||
|
|
||||||
|
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
Reference in New Issue
Block a user