bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-07-21 17:32:08 +08:00
commit 8a5629924f
28 changed files with 293 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

47
.gitattributes vendored Normal file
View File

@@ -0,0 +1,47 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bin.* filter=lfs diff=lfs merge=lfs -text
*.bz2 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
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack 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
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
saved_model/**/* 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
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zstandard filter=lfs diff=lfs merge=lfs -text
*.tfevents* filter=lfs diff=lfs merge=lfs -text
*.db* filter=lfs diff=lfs merge=lfs -text
*.ark* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.gguf* filter=lfs diff=lfs merge=lfs -text
*.ggml filter=lfs diff=lfs merge=lfs -text
*.llamafile* filter=lfs diff=lfs merge=lfs -text
*.pt2 filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text

173
README.md Normal file
View File

@@ -0,0 +1,173 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
tags:
- language
- granite-3.3
license: apache-2.0
inference: false
base_model: ibm-granite/granite-3.3-2b-instruct
base_model_relation: quantized
---
## Llamacpp imatrix Quantizations of granite-3.3-2b-instruct by ibm-granite
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5147">b5147</a> for quantization.
Original model: https://huggingface.co/ibm-granite/granite-3.3-2b-instruct
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
```
<|start_of_role|>system<|end_of_role|>{system_prompt}<|end_of_text|>
<|start_of_role|>user<|end_of_role|>{prompt}<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [granite-3.3-2b-instruct-bf16.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-bf16.gguf) | bf16 | 5.07GB | false | Full BF16 weights. |
| [granite-3.3-2b-instruct-Q8_0.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q8_0.gguf) | Q8_0 | 2.69GB | false | Extremely high quality, generally unneeded but max available quant. |
| [granite-3.3-2b-instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q6_K_L.gguf) | Q6_K_L | 2.10GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [granite-3.3-2b-instruct-Q6_K.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q6_K.gguf) | Q6_K | 2.08GB | false | Very high quality, near perfect, *recommended*. |
| [granite-3.3-2b-instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q5_K_L.gguf) | Q5_K_L | 1.83GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [granite-3.3-2b-instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q5_K_M.gguf) | Q5_K_M | 1.80GB | false | High quality, *recommended*. |
| [granite-3.3-2b-instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q5_K_S.gguf) | Q5_K_S | 1.76GB | false | High quality, *recommended*. |
| [granite-3.3-2b-instruct-Q4_1.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q4_1.gguf) | Q4_1 | 1.61GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [granite-3.3-2b-instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q4_K_L.gguf) | Q4_K_L | 1.57GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [granite-3.3-2b-instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q4_K_M.gguf) | Q4_K_M | 1.55GB | false | Good quality, default size for most use cases, *recommended*. |
| [granite-3.3-2b-instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q4_K_S.gguf) | Q4_K_S | 1.46GB | false | Slightly lower quality with more space savings, *recommended*. |
| [granite-3.3-2b-instruct-Q4_0.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q4_0.gguf) | Q4_0 | 1.46GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [granite-3.3-2b-instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ4_NL.gguf) | IQ4_NL | 1.46GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [granite-3.3-2b-instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ4_XS.gguf) | IQ4_XS | 1.38GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [granite-3.3-2b-instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q3_K_XL.gguf) | Q3_K_XL | 1.38GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [granite-3.3-2b-instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q3_K_L.gguf) | Q3_K_L | 1.36GB | false | Lower quality but usable, good for low RAM availability. |
| [granite-3.3-2b-instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q3_K_M.gguf) | Q3_K_M | 1.25GB | false | Low quality. |
| [granite-3.3-2b-instruct-IQ3_M.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ3_M.gguf) | IQ3_M | 1.17GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [granite-3.3-2b-instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q3_K_S.gguf) | Q3_K_S | 1.13GB | false | Low quality, not recommended. |
| [granite-3.3-2b-instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ3_XS.gguf) | IQ3_XS | 1.08GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [granite-3.3-2b-instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ3_XXS.gguf) | IQ3_XXS | 1.01GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [granite-3.3-2b-instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q2_K_L.gguf) | Q2_K_L | 1.00GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [granite-3.3-2b-instruct-Q2_K.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-Q2_K.gguf) | Q2_K | 0.98GB | false | Very low quality but surprisingly usable. |
| [granite-3.3-2b-instruct-IQ2_M.gguf](https://huggingface.co/bartowski/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/ibm-granite_granite-3.3-2b-instruct-IQ2_M.gguf) | IQ2_M | 0.89GB | 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/ibm-granite_granite-3.3-2b-instruct-GGUF --include "ibm-granite_granite-3.3-2b-instruct-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/ibm-granite_granite-3.3-2b-instruct-GGUF --include "ibm-granite_granite-3.3-2b-instruct-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (ibm-granite_granite-3.3-2b-instruct-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
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

3
ibm-granite_granite-3.3-2b-instruct-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:08bdccf9dd7cd4a378a6c280f8a828ab18c6a47d6f832d2832f3347261512c65
size 888532672

3
ibm-granite_granite-3.3-2b-instruct-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:9b38effafc86a641e5e33a4ef0f3ee5774f19ac2ccdd52d287ef3ca036c767b9
size 1169290816

3
ibm-granite_granite-3.3-2b-instruct-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:aa5ae87946c922915736859dd6fc3bdd4b39005608bcfea81fa4a3cec1007a0c
size 1078851136

3
ibm-granite_granite-3.3-2b-instruct-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a525a74e9f9febe029caa228fd11246e18e4d383f96fd782b7a1417c5aef2c6f
size 1005186752

3
ibm-granite_granite-3.3-2b-instruct-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0df19f03b7f248b16682255bf3a7f340557580cf54ddd9afa0f3dfdb58b64ba1
size 1458632256

3
ibm-granite_granite-3.3-2b-instruct-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c13e05f57bd75ccd6241928ded9689ae4a7f51ad931695f850b0cf9304b6fccd
size 1383921216

3
ibm-granite_granite-3.3-2b-instruct-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:587a46fe07f02b0cfb1cb8188306c467f210674dcd9028fde150c5d78cd9ee67
size 978253376

3
ibm-granite_granite-3.3-2b-instruct-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3261aa0c63e7e0d53d73b4a1d92c0a0362e2cc3f8f59062ae2b89feeb6585630
size 1002636224

3
ibm-granite_granite-3.3-2b-instruct-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:49a713d912774522b67593a3f7fb290cbd8edd99b290460f0d972c283a8ab77f
size 1357379136

3
ibm-granite_granite-3.3-2b-instruct-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f2cb2782e06d8f79b89ae0e29e43dfdbc875c2e7c6a433c0948a89da1710c4f2
size 1251735104

3
ibm-granite_granite-3.3-2b-instruct-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:40b83cf008a03342f10386bd064601d9d42381e7814ce6f11cb5bb2a5b446a04
size 1130296896

3
ibm-granite_granite-3.3-2b-instruct-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6ac77d1d61f8983ae1524485168d47ffd2bf2a94342d492bd3f7e9c8f027477d
size 1381761984

3
ibm-granite_granite-3.3-2b-instruct-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:417330dc64f62dde19b285952097b60f2f5a04f73ef7c87d99345749d6d8a896
size 1458632256

3
ibm-granite_granite-3.3-2b-instruct-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:256c30073c3abfe4690489e5d1bf6f9321e96e7a57a02ba39587e435acfb4659
size 1605432896

3
ibm-granite_granite-3.3-2b-instruct-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:14fd5b193376ac7b31a6bdb45f5488a781476a59a32662406ebef5eb319b5be2
size 1569686464

3
ibm-granite_granite-3.3-2b-instruct-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:555b91485955bc96eb445b57dd4bbf8809aa7d8cce7c313f4f8bc5b2340896b4
size 1545303616

3
ibm-granite_granite-3.3-2b-instruct-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5712c30774a369040664da3749ef47508e11504f0c36acf6c66356a446b1b1af
size 1464399424

3
ibm-granite_granite-3.3-2b-instruct-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ac6a7d44f83cf11ccce3459cbc16f85b9226ae428678d2456ffdc5a73e851d7a
size 1829209024

3
ibm-granite_granite-3.3-2b-instruct-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1cea20687ecc06a9bc4fca281bcca5a13e6953c2881308abb7fae352a743259f
size 1804826176

3
ibm-granite_granite-3.3-2b-instruct-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:67be532ede40781dd92d34337ab9c9a5b78894d88c2a4883af9ab9da003562dd
size 1757476416

3
ibm-granite_granite-3.3-2b-instruct-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0ed1776f322ed46c462460bbaac28e465d4b5a091f6db7d127107f2c546898ca
size 2080568896

3
ibm-granite_granite-3.3-2b-instruct-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:56f980677d86b7f48ac405e21c42fa925991e725f381390fcfd233b566445290
size 2104951744

3
ibm-granite_granite-3.3-2b-instruct-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:057edd409b4d56773c90587655300d848d467cfec72c5529f9bab9ba46be0775
size 2694120384

3
ibm-granite_granite-3.3-2b-instruct-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:da687838c084d01dbcc218c1ffc945e2be3e0c4b16811f0ddce4959205fa0793
size 5069158176

BIN
ibm-granite_granite-3.3-2b-instruct.imatrix Normal file
View File

Binary file not shown.