初始化项目,由ModelHub XC社区提供模型

Model: bartowski/G2-9B-Aletheia-v1-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-09-15 06:18:12 +08:00
commit 1b1f371595
28 changed files with 266 additions and 0 deletions

60
.gitattributes vendored Normal file
View File

@@ -0,0 +1,60 @@
*.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
G2-9B-Aletheia-v1-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q6_K_L.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q5_K_L.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_K_L.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_0_8_8.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_0_4_8.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_0_4_4.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q3_K_XL.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-IQ3_XS.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q2_K_L.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1-f16.gguf filter=lfs diff=lfs merge=lfs -text
G2-9B-Aletheia-v1.imatrix filter=lfs diff=lfs merge=lfs -text

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d5facb0e5b47a1d8ccf056c5ff917c64b482e2b37cdd271ebb884580c526a35c
size 3434670240

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cc5de1af9df6678febd4a4fcdc53168ac42b0b52c5e2bca7536bbcc4abaf6b85
size 4494616736

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:81786ba5ccc1b808d98b8486b39a65ea3ef79b0ad03046eb2fa8d72b89b344c6
size 4144990368

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1f12819650627571d535abedf209e5feba42b9508ebcf863fcc5e0b3a82118f9
size 5183031456

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7e4f59fcba0e5efbdedee01a5232de292876a1eae4d2462397b9cd72944658be
size 3805399200

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2cc8a8d90ee2495f662af237d39ac7dcb4a0b57dc7a152bbf1fc1f9ac867bd4f
size 4027607200

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:87f560176e7cce6743a0406175b9ce22322486989845c094288cf6b72fe24431
size 5132454048

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:53962cdb5b60e6a45b0a82a92c924517252d7baec96aa9e1361270b9e7a2ff81
size 4761782432

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1916ba91ab813d21d440dfda8f42a166a231ec6d43ff211427c3d54a26fbda24
size 4337666208

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:be923314bfe0fcadbbd3d139785f530be7d7d12eacb77dd86d67ea52c27ad7ac
size 5354662048

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0491e8e279473526117772d7651650e34b09bd16202059f663d07dd8c936df94
size 5459200160

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ce51a99e4f757d2dc4bd0c6b7750aef1536ffd26543d18f9ce7f555f83ddd079
size 5443143840

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:78ac755607cdd20b0488f45ec6ac49c872387add1ec8681113118f88567304e6
size 5443143840

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:63dd4cac96d28cb00b1d1ba7e8d02648e24c91cddd0d99db75748069478e4567
size 5443143840

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:09c444e20b26e446b47421daaa42af4ca0d8085800ca474a58ea50aa149d4443
size 5983266976

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:786a8eec95cf0d4862255fa3a18f3a100296fdbfd67995f0767301cd7c056baf
size 5761058976

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:23664f73a23778f1309d63ac5e395b6318cd45bea9bd3783abfaa582b981166b
size 5478926496

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:bd396ec2adab17ae1580d61b1ab928d339b3e4e330a5f97641d38be7909f2171
size 6869575840

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8b0e663edd0960eecada2958649881590c5e75db6033ce5cfda123d35f8bcd9e
size 6647367840

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:fb155e429b79e825e376007627f5c0cce0fea695cd3e5f2401d3c9db7cd071de
size 6483593376

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:85ec303dcf4c19b666a0bab443bf7cdb5394da9c29cc82ca5565dbf975def0ba
size 7589071008

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:dbefb74f567d43ad7103f85e28d2886d66ce94c4ec4b00633ce2bdad3cd93648
size 7811279008

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e52420e842a39957267cf49332441344f491f4858a1b1c88a9a320d18603fee4
size 9827149984

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5e42c1cc93b77c16f8302bad55ffd9563319941832e05b33b943a2fb5acef60f
size 18490681248

View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c3486aeaea65587bc619eb7858641227c30a7e17224831b0ebbea1e11d7e370b
size 6116900

130
README.md Normal file
View File

@@ -0,0 +1,130 @@
---
base_model: allura-org/G2-9B-Aletheia-v1
license: gemma
pipeline_tag: text-generation
tags:
- mergekit
- merge
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of G2-9B-Aletheia-v1
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4014">b4014</a> for quantization.
Original model: https://huggingface.co/allura-org/G2-9B-Aletheia-v1
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
```
<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
<end_of_turn>
<start_of_turn>model
```
Note that this model does not support a System prompt.
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [G2-9B-Aletheia-v1-f16.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-f16.gguf) | f16 | 18.49GB | false | Full F16 weights. |
| [G2-9B-Aletheia-v1-Q8_0.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q8_0.gguf) | Q8_0 | 9.83GB | false | Extremely high quality, generally unneeded but max available quant. |
| [G2-9B-Aletheia-v1-Q6_K_L.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q6_K_L.gguf) | Q6_K_L | 7.81GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [G2-9B-Aletheia-v1-Q6_K.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q6_K.gguf) | Q6_K | 7.59GB | false | Very high quality, near perfect, *recommended*. |
| [G2-9B-Aletheia-v1-Q5_K_L.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q5_K_L.gguf) | Q5_K_L | 6.87GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [G2-9B-Aletheia-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q5_K_M.gguf) | Q5_K_M | 6.65GB | false | High quality, *recommended*. |
| [G2-9B-Aletheia-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q5_K_S.gguf) | Q5_K_S | 6.48GB | false | High quality, *recommended*. |
| [G2-9B-Aletheia-v1-Q4_K_L.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_K_L.gguf) | Q4_K_L | 5.98GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [G2-9B-Aletheia-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_K_M.gguf) | Q4_K_M | 5.76GB | false | Good quality, default size for must use cases, *recommended*. |
| [G2-9B-Aletheia-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_K_S.gguf) | Q4_K_S | 5.48GB | false | Slightly lower quality with more space savings, *recommended*. |
| [G2-9B-Aletheia-v1-Q4_0.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_0.gguf) | Q4_0 | 5.46GB | false | Legacy format, generally not worth using over similarly sized formats |
| [G2-9B-Aletheia-v1-Q4_0_8_8.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_0_8_8.gguf) | Q4_0_8_8 | 5.44GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [G2-9B-Aletheia-v1-Q4_0_4_8.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_0_4_8.gguf) | Q4_0_4_8 | 5.44GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
| [G2-9B-Aletheia-v1-Q4_0_4_4.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q4_0_4_4.gguf) | Q4_0_4_4 | 5.44GB | 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*. |
| [G2-9B-Aletheia-v1-Q3_K_XL.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q3_K_XL.gguf) | Q3_K_XL | 5.35GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [G2-9B-Aletheia-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-IQ4_XS.gguf) | IQ4_XS | 5.18GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [G2-9B-Aletheia-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q3_K_L.gguf) | Q3_K_L | 5.13GB | false | Lower quality but usable, good for low RAM availability. |
| [G2-9B-Aletheia-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q3_K_M.gguf) | Q3_K_M | 4.76GB | false | Low quality. |
| [G2-9B-Aletheia-v1-IQ3_M.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-IQ3_M.gguf) | IQ3_M | 4.49GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [G2-9B-Aletheia-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q3_K_S.gguf) | Q3_K_S | 4.34GB | false | Low quality, not recommended. |
| [G2-9B-Aletheia-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-IQ3_XS.gguf) | IQ3_XS | 4.14GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [G2-9B-Aletheia-v1-Q2_K_L.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q2_K_L.gguf) | Q2_K_L | 4.03GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [G2-9B-Aletheia-v1-Q2_K.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-Q2_K.gguf) | Q2_K | 3.81GB | false | Very low quality but surprisingly usable. |
| [G2-9B-Aletheia-v1-IQ2_M.gguf](https://huggingface.co/bartowski/G2-9B-Aletheia-v1-GGUF/blob/main/G2-9B-Aletheia-v1-IQ2_M.gguf) | IQ2_M | 3.43GB | 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.
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/G2-9B-Aletheia-v1-GGUF --include "G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF --include "G2-9B-Aletheia-v1-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (G2-9B-Aletheia-v1-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}