bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-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/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-10-12 01:55:13 +08:00
commit 9af1c44a0f
28 changed files with 287 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

167
README.md Normal file
View File

@@ -0,0 +1,167 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: andrewzh/Absolute_Zero_Reasoner-Coder-3b
base_model_relation: quantized
---
## Llamacpp imatrix Quantizations of Absolute_Zero_Reasoner-Coder-3b by andrewzh
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5338">b5338</a> for quantization.
Original model: https://huggingface.co/andrewzh/Absolute_Zero_Reasoner-Coder-3b
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
```
{system_prompt}
{prompt}
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Absolute_Zero_Reasoner-Coder-3b-bf16.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-bf16.gguf) | bf16 | 6.18GB | false | Full BF16 weights. |
| [Absolute_Zero_Reasoner-Coder-3b-Q8_0.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q8_0.gguf) | Q8_0 | 3.29GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Absolute_Zero_Reasoner-Coder-3b-Q6_K_L.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q6_K_L.gguf) | Q6_K_L | 2.61GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q6_K.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q6_K.gguf) | Q6_K | 2.54GB | false | Very high quality, near perfect, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q5_K_L.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_L.gguf) | Q5_K_L | 2.30GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q5_K_M.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_M.gguf) | Q5_K_M | 2.22GB | false | High quality, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q5_K_S.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_S.gguf) | Q5_K_S | 2.17GB | false | High quality, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q4_K_L.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_L.gguf) | Q4_K_L | 2.01GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q4_1.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_1.gguf) | Q4_1 | 2.00GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Absolute_Zero_Reasoner-Coder-3b-Q4_K_M.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_M.gguf) | Q4_K_M | 1.93GB | false | Good quality, default size for most use cases, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q4_K_S.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_S.gguf) | Q4_K_S | 1.83GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q4_0.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_0.gguf) | Q4_0 | 1.83GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ4_NL.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ4_NL.gguf) | IQ4_NL | 1.83GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Absolute_Zero_Reasoner-Coder-3b-Q3_K_XL.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_XL.gguf) | Q3_K_XL | 1.78GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ4_XS.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ4_XS.gguf) | IQ4_XS | 1.74GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Absolute_Zero_Reasoner-Coder-3b-Q3_K_L.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_L.gguf) | Q3_K_L | 1.71GB | false | Lower quality but usable, good for low RAM availability. |
| [Absolute_Zero_Reasoner-Coder-3b-Q3_K_M.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_M.gguf) | Q3_K_M | 1.59GB | false | Low quality. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ3_M.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_M.gguf) | IQ3_M | 1.49GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Absolute_Zero_Reasoner-Coder-3b-Q3_K_S.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_S.gguf) | Q3_K_S | 1.45GB | false | Low quality, not recommended. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ3_XS.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_XS.gguf) | IQ3_XS | 1.39GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Absolute_Zero_Reasoner-Coder-3b-Q2_K_L.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q2_K_L.gguf) | Q2_K_L | 1.35GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ3_XXS.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_XXS.gguf) | IQ3_XXS | 1.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Absolute_Zero_Reasoner-Coder-3b-Q2_K.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q2_K.gguf) | Q2_K | 1.27GB | false | Very low quality but surprisingly usable. |
| [Absolute_Zero_Reasoner-Coder-3b-IQ2_M.gguf](https://huggingface.co/bartowski/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF/blob/main/andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ2_M.gguf) | IQ2_M | 1.14GB | 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/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF --include "andrewzh_Absolute_Zero_Reasoner-Coder-3b-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/andrewzh_Absolute_Zero_Reasoner-Coder-3b-GGUF --include "andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (andrewzh_Absolute_Zero_Reasoner-Coder-3b-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

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:241cd4954ca6814415704b74cb261ada829fab88ac50b395a5ea052e185c3a39
size 1140513024

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:22cdd54304309912bf91342cf14eb85a57c93e17febc0c696bff719ed1e533d0
size 1488892160

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:41880892c36786862a2643938a35af6ee54c6a1e7c4111dcfe4caf5ef56e5085
size 1391833344

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3336aa056ab3bf9dd1d2bf086c96b82610aadca2b014db7d0a3b21333a174d1a
size 1282824448

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5d107e455a333c154c3095b8d5899ee9745fc2fc8f93d08c98816be118e94f9e
size 1825206528

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:12fc3d006710804a4d333e754fa6beef4e14651dd7594702299d6b6b128d1058
size 1739092224

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:15e6a84678799947bf0d042a458183f51d09995e50cf62d28d282011782d5eeb
size 1274753280

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f37d55cf7a412a2e837f27f5e2ade8a4c3303a56e376ee3414c03d72f51d4b72
size 1350113536

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:58de8c05288d928431420fafc280b1edac32638b999e461fd6c26a7858e19889
size 1707389184

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:712ed994d3c4f5862bc02165ad19d687be15f20d321a8010e44c98daa8811758
size 1590472960

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7e7d80cf95bf82dca850d7db435bf4a5a5abf68a81a61dd9bbf9dbe9d5e41abd
size 1454354688

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:01a8a1259ba974f47976328bfab93d02fb5ef54baf5853713c3297dfb71a907b
size 1782749440

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0d34d7028e4a259ba17cfd936b8fec5f993e772dff10619cb20c88d681411546
size 1828483328

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7603b11226071b4da291664098e3c55b9cc78629d05b47b7ddd3de752f439ecc
size 1996255488

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d7a7d3d8550cdc280ada9a15f47be9ea02467e19a083c568051355643d17e9a6
size 2005260544

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:45fa336bf05499d5fec5164cb5aa2819004fa66390c369a7d38116b47d44832a
size 1929900288

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:49d8f1d1eee00e345783ac2fbb3b1a74675bc322b4b82be1ea3458fb31e71a6c
size 1834381568

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5005cec9531e46b8c1027722b9b67075e6a954c8aa20523307487eb02dd3a9e0
size 2300172544

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c3872fb4c6ca8176b7f55df8414a484c13f95df106cec07eb631e8394203462e
size 2224812288

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5e2efe91edee6d6c6d9e6bd57958f87c0367d8c7aed616aab4dd5c1933f32195
size 2169663744

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:19dc41b54527de387dc5bdfe755520db61fed4efc82ff537c659fa24d651e974
size 2538156288

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ad96e16b9ef2429a576215acfee28f9327c4f19241945a93f1ec95f811dcd950
size 2613516544

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0e616b88dfb3b6118b224323381b98228b6b68752d7514ba01b1caaac3f5e5b8
size 3285473536

3
andrewzh_Absolute_Zero_Reasoner-Coder-3b-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:318c3f8f5cf866d8e7989d69e246de626ba1be9483d026763c513d1422eb8e5b
size 6178314176

BIN
andrewzh_Absolute_Zero_Reasoner-Coder-3b.imatrix Normal file
View File

Binary file not shown.

1
configuration.json Normal file
View File

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