初始化项目,由ModelHub XC社区提供模型

Model: bartowski/granite-3.0-2b-instruct-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-07-19 01:13:08 +08:00
commit ec4bfe72bd
27 changed files with 378 additions and 0 deletions

59
.gitattributes vendored Normal file
View File

@@ -0,0 +1,59 @@
*.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
granite-3.0-2b-instruct-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q6_K_L.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q5_K_L.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_K_L.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_0_8_8.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_0_4_8.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_0_4_4.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q3_K_XL.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-IQ3_XS.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q2_K_L.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct-f16.gguf filter=lfs diff=lfs merge=lfs -text
granite-3.0-2b-instruct.imatrix filter=lfs diff=lfs merge=lfs -text

246
README.md Normal file
View File

@@ -0,0 +1,246 @@
---
base_model: ibm-granite/granite-3.0-2b-instruct
license: apache-2.0
pipeline_tag: text-generation
tags:
- language
- granite-3.0
quantized_by: bartowski
inference: false
model-index:
- name: granite-3.0-2b-instruct
results:
- task:
type: text-generation
dataset:
name: IFEval
type: instruction-following
metrics:
- type: pass@1
value: 46.07
name: pass@1
- type: pass@1
value: 7.66
name: pass@1
- task:
type: text-generation
dataset:
name: AGI-Eval
type: human-exams
metrics:
- type: pass@1
value: 29.75
name: pass@1
- type: pass@1
value: 56.03
name: pass@1
- type: pass@1
value: 27.92
name: pass@1
- task:
type: text-generation
dataset:
name: OBQA
type: commonsense
metrics:
- type: pass@1
value: 43.2
name: pass@1
- type: pass@1
value: 66.36
name: pass@1
- type: pass@1
value: 76.79
name: pass@1
- type: pass@1
value: 71.9
name: pass@1
- type: pass@1
value: 53.37
name: pass@1
- task:
type: text-generation
dataset:
name: BoolQ
type: reading-comprehension
metrics:
- type: pass@1
value: 84.89
name: pass@1
- type: pass@1
value: 19.73
name: pass@1
- task:
type: text-generation
dataset:
name: ARC-C
type: reasoning
metrics:
- type: pass@1
value: 54.35
name: pass@1
- type: pass@1
value: 28.61
name: pass@1
- type: pass@1
value: 43.74
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis
type: code
metrics:
- type: pass@1
value: 50.61
name: pass@1
- type: pass@1
value: 45.58
name: pass@1
- type: pass@1
value: 51.83
name: pass@1
- type: pass@1
value: 41.0
name: pass@1
- task:
type: text-generation
dataset:
name: GSM8K
type: math
metrics:
- type: pass@1
value: 59.66
name: pass@1
- type: pass@1
value: 23.66
name: pass@1
- task:
type: text-generation
dataset:
name: PAWS-X (7 langs)
type: multilingual
metrics:
- type: pass@1
value: 61.42
name: pass@1
- type: pass@1
value: 37.13
name: pass@1
---
## Llamacpp imatrix Quantizations of granite-3.0-2b-instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3930">b3930</a> for quantization.
Original model: https://huggingface.co/ibm-granite/granite-3.0-2b-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/)
## 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-3.0-2b-instruct-f16.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-f16.gguf) | f16 | 5.27GB | false | Full F16 weights. |
| [granite-3.0-2b-instruct-Q8_0.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q8_0.gguf) | Q8_0 | 2.80GB | false | Extremely high quality, generally unneeded but max available quant. |
| [granite-3.0-2b-instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q6_K_L.gguf) | Q6_K_L | 2.21GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [granite-3.0-2b-instruct-Q6_K.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q6_K.gguf) | Q6_K | 2.16GB | false | Very high quality, near perfect, *recommended*. |
| [granite-3.0-2b-instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q5_K_L.gguf) | Q5_K_L | 1.94GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [granite-3.0-2b-instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q5_K_M.gguf) | Q5_K_M | 1.87GB | false | High quality, *recommended*. |
| [granite-3.0-2b-instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q5_K_S.gguf) | Q5_K_S | 1.83GB | false | High quality, *recommended*. |
| [granite-3.0-2b-instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_K_L.gguf) | Q4_K_L | 1.68GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [granite-3.0-2b-instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_K_M.gguf) | Q4_K_M | 1.60GB | false | Good quality, default size for must use cases, *recommended*. |
| [granite-3.0-2b-instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_K_S.gguf) | Q4_K_S | 1.52GB | false | Slightly lower quality with more space savings, *recommended*. |
| [granite-3.0-2b-instruct-Q4_0.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_0.gguf) | Q4_0 | 1.52GB | false | Legacy format, generally not worth using over similarly sized formats |
| [granite-3.0-2b-instruct-Q4_0_8_8.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_0_8_8.gguf) | Q4_0_8_8 | 1.51GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [granite-3.0-2b-instruct-Q4_0_4_8.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_0_4_8.gguf) | Q4_0_4_8 | 1.51GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
| [granite-3.0-2b-instruct-Q4_0_4_4.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q4_0_4_4.gguf) | Q4_0_4_4 | 1.51GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. *Don't use on Mac or Windows*. |
| [granite-3.0-2b-instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q3_K_XL.gguf) | Q3_K_XL | 1.49GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [granite-3.0-2b-instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-IQ4_XS.gguf) | IQ4_XS | 1.44GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [granite-3.0-2b-instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q3_K_L.gguf) | Q3_K_L | 1.40GB | false | Lower quality but usable, good for low RAM availability. |
| [granite-3.0-2b-instruct-IQ3_M.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-IQ3_M.gguf) | IQ3_M | 1.21GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [granite-3.0-2b-instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q3_K_S.gguf) | Q3_K_S | 1.17GB | false | Low quality, not recommended. |
| [granite-3.0-2b-instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-IQ3_XS.gguf) | IQ3_XS | 1.12GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [granite-3.0-2b-instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q2_K_L.gguf) | Q2_K_L | 1.11GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [granite-3.0-2b-instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-IQ3_XXS.gguf) | IQ3_XXS | 1.05GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [granite-3.0-2b-instruct-Q2_K.gguf](https://huggingface.co/bartowski/granite-3.0-2b-instruct-GGUF/blob/main/granite-3.0-2b-instruct-Q2_K.gguf) | Q2_K | 1.01GB | false | Very low quality but 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.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
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/granite-3.0-2b-instruct-GGUF --include "granite-3.0-2b-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/granite-3.0-2b-instruct-GGUF --include "granite-3.0-2b-instruct-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (granite-3.0-2b-instruct-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
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.
## 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
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}

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7ebd38b61aa7262a99fdec2ad7be1e9fe9cc542b1dd3912fcbe7c275250ad90c
size 1212536736

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6c85e91f1dcee6412716b2972dbeb21946b798a3d96265bd92374900eb4f9b22
size 1122097056

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b4aa6a152336b6765c2eb011ecede502313603f795c5323b517734506fcbca58
size 1048433760

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:df14ad56cbfe2ab945af856bb227624eebff9f65434fbc7f0a18eff6e946cf77
size 1437391360

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:eeea2a4a38168e764a08b90fa1d1840492990e1f03d3a30fb50f6607e867618b
size 1011275040

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c79c6640440848bcfea13040f2b97863a077fc6475f81725010ed77fcb84f2b5
size 1109585024

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:63e88257a872e63589e3a99ed2ce869edd91ca7c9ec75e7338b17c7a74b9c2d8
size 1400625056

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:371675079b78881eafd9f104aa4ff345d2b043a0d0d6d213d495e694bedd1569
size 1173542816

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b7f87b039bfb39159de6be10dfcc29e5280952f1fd8a2658a8144e7406a6de8b
size 1488710784

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:95852ef01e544c274b22870c1c723a835fb9ec8bd25d54121f62053b4bf8b985
size 1515248320

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ab5b25e63718b643f20e594f696ef56490fa95a76c7b0f4f91f8151a5f6b0abc
size 1510005440

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:61a35c46aa92c1671286fc27af892b533cc7b963916960828ce560366123c96e
size 1510005440

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f77eda5e70139eb5a0988aa90db3cd2f526b51f37707eea93ab351217e969c09
size 1510005440

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:00befb6c6b001d0afc11878c9e763a3fec9f6e6c9e2abbeb9ad90c71060dbfa4
size 1676635264

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:06e5f1fdb176a2d79a2f33febfbbd521935fc0c00573326e91439b7d21e89df0
size 1601919680

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c59e85c08f21eaa6cac64db7c6c1f12a0af4252eb75c9b2a163bfcd82a6269e1
size 1521015488

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e242de3eef3c5cd512db2d9999d26cbed86b93979631ac33eb8af2164a27838a
size 1936157824

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:74f28e91ece98a49bbd6d40442502258e0ba31e9119743344d9b447b8542dc1f
size 1874025920

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:770552d5ea153f12e9780918ac39a44b5c95f9110cb2297f4c40b975e9780dc8
size 1826676160

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:aa582b105edd92fe44043e855a36cbe9a06a18e2769bef773cf350d80171319d
size 2163138816

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:faf8669ea9428b4b6c91ca6c2a704ad9964d26bf213c058294445006b467114d
size 2211900544

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f0c1e2a078540bd2721d117d456906b9e6ad1da0c27ed0ab552e762fa9371adb
size 2801069184

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4196ce2fcd737522a49de889ad14268b1ef58c097ef042240bb87041c5a6e219
size 5270476896

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ac82657af231884d9086f50c756a0da974920a6ca59137649e07bafa61729873
size 3286098