初始化项目，由ModelHub XC社区提供模型

Model: bartowski/nvidia_AceInstruct-1.5B-GGUF Source: Original Platform
2026-09-19 16:10:13 +08:00
commit 7321643de7
29 changed files with 322 additions and 0 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1,61 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-f16.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q6_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q5_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q4_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q4_1.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ4_NL.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q3_K_XL.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ3_XS.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q2_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B-f32.gguf filter=lfs diff=lfs merge=lfs -text
+nvidia_AceInstruct-1.5B.imatrix filter=lfs diff=lfs merge=lfs -text
--- a/README.md
+++ b/README.md
@@ -0,0 +1,182 @@
+---
+quantized_by: bartowski
+pipeline_tag: text-generation
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- nvidia
+- AceInstruct
+- code
+- math
+- general_domain
+- instruct_model
+base_model: nvidia/AceInstruct-1.5B
+---
+
+## Llamacpp imatrix Quantizations of AceInstruct-1.5B by nvidia
+
+Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4688">b4688</a> for quantization.
+
+Original model: https://huggingface.co/nvidia/AceInstruct-1.5B
+
+All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
+
+Run them in [LM Studio](https://lmstudio.ai/)
+
+Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
+
+## Prompt format
+
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+
+## Download a file (not the whole branch) from below:
+
+| Filename | Quant type | File Size | Split | Description |
+| -------- | ---------- | --------- | ----- | ----------- |
+| [AceInstruct-1.5B-f32.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-f32.gguf) | f32 | 7.11GB | false | Full F32 weights. |
+| [AceInstruct-1.5B-f16.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-f16.gguf) | f16 | 3.56GB | false | Full F16 weights. |
+| [AceInstruct-1.5B-Q8_0.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q8_0.gguf) | Q8_0 | 1.89GB | false | Extremely high quality, generally unneeded but max available quant. |
+| [AceInstruct-1.5B-Q6_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q6_K_L.gguf) | Q6_K_L | 1.58GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
+| [AceInstruct-1.5B-Q6_K.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q6_K.gguf) | Q6_K | 1.46GB | false | Very high quality, near perfect, *recommended*. |
+| [AceInstruct-1.5B-Q5_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q5_K_L.gguf) | Q5_K_L | 1.43GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
+| [AceInstruct-1.5B-Q5_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q5_K_M.gguf) | Q5_K_M | 1.29GB | false | High quality, *recommended*. |
+| [AceInstruct-1.5B-Q4_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q4_K_L.gguf) | Q4_K_L | 1.29GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
+| [AceInstruct-1.5B-Q5_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q5_K_S.gguf) | Q5_K_S | 1.26GB | false | High quality, *recommended*. |
+| [AceInstruct-1.5B-Q3_K_XL.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q3_K_XL.gguf) | Q3_K_XL | 1.18GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
+| [AceInstruct-1.5B-Q4_1.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q4_1.gguf) | Q4_1 | 1.16GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
+| [AceInstruct-1.5B-Q4_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q4_K_M.gguf) | Q4_K_M | 1.12GB | false | Good quality, default size for most use cases, *recommended*. |
+| [AceInstruct-1.5B-Q4_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q4_K_S.gguf) | Q4_K_S | 1.07GB | false | Slightly lower quality with more space savings, *recommended*. |
+| [AceInstruct-1.5B-Q4_0.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q4_0.gguf) | Q4_0 | 1.07GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
+| [AceInstruct-1.5B-IQ4_NL.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ4_NL.gguf) | IQ4_NL | 1.07GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
+| [AceInstruct-1.5B-IQ4_XS.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ4_XS.gguf) | IQ4_XS | 1.02GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
+| [AceInstruct-1.5B-Q3_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q3_K_L.gguf) | Q3_K_L | 0.98GB | false | Lower quality but usable, good for low RAM availability. |
+| [AceInstruct-1.5B-Q2_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q2_K_L.gguf) | Q2_K_L | 0.98GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [AceInstruct-1.5B-Q3_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q3_K_M.gguf) | Q3_K_M | 0.92GB | false | Low quality. |
+| [AceInstruct-1.5B-IQ3_M.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ3_M.gguf) | IQ3_M | 0.88GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
+| [AceInstruct-1.5B-Q3_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q3_K_S.gguf) | Q3_K_S | 0.86GB | false | Low quality, not recommended. |
+| [AceInstruct-1.5B-IQ3_XS.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ3_XS.gguf) | IQ3_XS | 0.83GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
+| [AceInstruct-1.5B-IQ3_XXS.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ3_XXS.gguf) | IQ3_XXS | 0.77GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
+| [AceInstruct-1.5B-Q2_K.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-Q2_K.gguf) | Q2_K | 0.75GB | false | Very low quality but surprisingly usable. |
+| [AceInstruct-1.5B-IQ2_M.gguf](https://huggingface.co/bartowski/nvidia_AceInstruct-1.5B-GGUF/blob/main/nvidia_AceInstruct-1.5B-IQ2_M.gguf) | IQ2_M | 0.70GB | false | Relatively low quality, uses SOTA techniques to be surprisingly 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/nvidia_AceInstruct-1.5B-GGUF --include "nvidia_AceInstruct-1.5B-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/nvidia_AceInstruct-1.5B-GGUF --include "nvidia_AceInstruct-1.5B-Q8_0/*" --local-dir ./
+```
+
+You can either specify a new local-dir (nvidia_AceInstruct-1.5B-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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
+
+The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
+
+</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
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
--- a/nvidia_AceInstruct-1.5B-IQ2_M.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ2_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3207bd7b0b27ed6ebf46264b9650f6452e2b5b38707d6446bee4f26725c06f
+size 701330208
--- a/nvidia_AceInstruct-1.5B-IQ3_M.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ3_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11120922f739906fbdfe867266610999b606af3fa0aa1e7c1b9508afcb9dda50
+size 876939552
--- a/nvidia_AceInstruct-1.5B-IQ3_XS.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ3_XS.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ef8d0d82d0c2f3bc6c7a50f3e490f31b0269b857fe4872c37029adb189b59c0
+size 831974688
--- a/nvidia_AceInstruct-1.5B-IQ3_XXS.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ3_XXS.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f8c49e2bb486ea6cfe55634f85ff2f5f3a16342da074202dfc7eee78b58cd1a
+size 769067808
--- a/nvidia_AceInstruct-1.5B-IQ4_NL.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ4_NL.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3938563994e49a8be2841830140889b39f343767e11bf5bb2c648ec052b2a79f
+size 1067601696
--- a/nvidia_AceInstruct-1.5B-IQ4_XS.gguf
+++ b/nvidia_AceInstruct-1.5B-IQ4_XS.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b995b47242fe1f2fdca4044ba553dbece37afce4707c35a4285b148eaf015d89
+size 1019709216
--- a/nvidia_AceInstruct-1.5B-Q2_K.gguf
+++ b/nvidia_AceInstruct-1.5B-Q2_K.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afb790b74418ec0959eb5dce8566634fd5f20f939f2ca1712c10f26f36a7aae3
+size 752878368
--- a/nvidia_AceInstruct-1.5B-Q2_K_L.gguf
+++ b/nvidia_AceInstruct-1.5B-Q2_K_L.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dd6b0f63756d8771d349629dbbd367338de9cafd65ce8b21a390859bd453741
+size 980782368
--- a/nvidia_AceInstruct-1.5B-Q3_K_L.gguf
+++ b/nvidia_AceInstruct-1.5B-Q3_K_L.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2af1a88d118ebc70606faf04e628b4d55ab38f99b598859355f574f347b77ed
+size 980438304
--- a/nvidia_AceInstruct-1.5B-Q3_K_M.gguf
+++ b/nvidia_AceInstruct-1.5B-Q3_K_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b29d7a8bf7a29c01bc08f1f8449d0a1b4194760a2f7db22ce970c6d27dfda1
+size 924454176
--- a/nvidia_AceInstruct-1.5B-Q3_K_S.gguf
+++ b/nvidia_AceInstruct-1.5B-Q3_K_S.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e78c1dfda2c708157dca81d7ff54a241161a39dfe97d5f6057909704529d086c
+size 861220128
--- a/nvidia_AceInstruct-1.5B-Q3_K_XL.gguf
+++ b/nvidia_AceInstruct-1.5B-Q3_K_XL.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3cfd54c133af72006c6b6caff095fae1ce7e37bfc97dba704a450603719bb38
+size 1184640288
--- a/nvidia_AceInstruct-1.5B-Q4_0.gguf
+++ b/nvidia_AceInstruct-1.5B-Q4_0.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b438c48d3551404a0a4cfd3b1bea7c20a1f5f5d2a8f5176f9981653d20f9f17
+size 1068805920
--- a/nvidia_AceInstruct-1.5B-Q4_1.gguf
+++ b/nvidia_AceInstruct-1.5B-Q4_1.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10a02a5ffa1d084c4154d4e6a3feac366bef35787c6f98f140d2b02f9d06b338
+size 1162698528
--- a/nvidia_AceInstruct-1.5B-Q4_K_L.gguf
+++ b/nvidia_AceInstruct-1.5B-Q4_K_L.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba7d5370834a6de9144b70d9c9e733c6a43aa0a6d955df03ba4047ad63f9fc13
+size 1290525984
--- a/nvidia_AceInstruct-1.5B-Q4_K_M.gguf
+++ b/nvidia_AceInstruct-1.5B-Q4_K_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:103b7fa617d2b3c2d6e168a878b9b5e3710d19d178bf4b890acf0fac2abafadb
+size 1117318944
--- a/nvidia_AceInstruct-1.5B-Q4_K_S.gguf
+++ b/nvidia_AceInstruct-1.5B-Q4_K_S.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f34f6ee993df95488fee2c3934c01cd9adb35fb8b0f0934d7073d940b2df4b
+size 1071583008
--- a/nvidia_AceInstruct-1.5B-Q5_K_L.gguf
+++ b/nvidia_AceInstruct-1.5B-Q5_K_L.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10f1b332f5b7f535bb31603fc33f1636dabe3624db5b214bc7d889086c0013c1
+size 1429527840
--- a/nvidia_AceInstruct-1.5B-Q5_K_M.gguf
+++ b/nvidia_AceInstruct-1.5B-Q5_K_M.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7575baddd4cfe8fddd96cdd289453f0740f6310d790ce51461dffd09536cd1da
+size 1285492512
--- a/nvidia_AceInstruct-1.5B-Q5_K_S.gguf
+++ b/nvidia_AceInstruct-1.5B-Q5_K_S.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63fc7e843cff49438f348ab7185b1d4e4b6634761d079656613b72afccb04bdc
+size 1259171616
--- a/nvidia_AceInstruct-1.5B-Q6_K.gguf
+++ b/nvidia_AceInstruct-1.5B-Q6_K.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3dda64085357f62baa4769145785e3ddf76929d78fb948f98b8402e68893af42
+size 1464176928
--- a/nvidia_AceInstruct-1.5B-Q6_K_L.gguf
+++ b/nvidia_AceInstruct-1.5B-Q6_K_L.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2170123b29eddf5d172f205b0ee85120193fd80ee561259b5c688c252bc7b19
+size 1577217312
--- a/nvidia_AceInstruct-1.5B-Q8_0.gguf
+++ b/nvidia_AceInstruct-1.5B-Q8_0.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d37b260f3ce3d8b8e37f1ff42f022144236a0bf41f098676d9b4b82ad42c3a4c
+size 1894530336
--- a/nvidia_AceInstruct-1.5B-f16.gguf
+++ b/nvidia_AceInstruct-1.5B-f16.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b3ac54259e93ed4f6de53c8f3b56f329430702c79e04b3c09d1345b4684e714
+size 3560414496
--- a/nvidia_AceInstruct-1.5B-f32.gguf
+++ b/nvidia_AceInstruct-1.5B-f32.gguf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0e6ebe5de53b5a53539fe62aa05240f3f01a53b673d01c5c44c3e967d0e828f
+size 7114300416
--- a/nvidia_AceInstruct-1.5B.imatrix
+++ b/nvidia_AceInstruct-1.5B.imatrix
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86446600001279a2499bc643cbbe7309fb9a9d4e6e4159527d80fd7e8e403863
+size 2042214
				`@@ -0,0 +1 @@`
				`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`