bartowski/arcee-ai_AFM-4.5B-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/arcee-ai_AFM-4.5B-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-08-19 19:51:12 +08:00
commit caa1e2cb4c
28 changed files with 317 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

194
README.md Normal file
View File

@@ -0,0 +1,194 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model_relation: quantized
base_model: arcee-ai/AFM-4.5B
license: apache-2.0
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
---
## Llamacpp imatrix Quantizations of AFM-4.5B by arcee-ai
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b6014">b6014</a> for quantization.
Original model: https://huggingface.co/arcee-ai/AFM-4.5B
All quants made using imatrix option with dataset combined_all_medium from Ed Addario [here](https://huggingface.co/datasets/eaddario/imatrix-calibration)
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
## Recommended sampling settings
* temperature: 0.5
* top_k: 50
* top_p: 0.95
* repeat_penalty: 1.1
* min_p: 0.05
Temperature set to 0.5 is the most important here, model tends to over talk with higher.
## 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 |
| -------- | ---------- | --------- | ----- | ----------- |
| [AFM-4.5B-bf16.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-bf16.gguf) | bf16 | 9.25GB | false | Full BF16 weights. |
| [AFM-4.5B-Q8_0.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q8_0.gguf) | Q8_0 | 4.92GB | false | Extremely high quality, generally unneeded but max available quant. |
| [AFM-4.5B-Q6_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q6_K_L.gguf) | Q6_K_L | 3.96GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [AFM-4.5B-Q6_K.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q6_K.gguf) | Q6_K | 3.80GB | false | Very high quality, near perfect, *recommended*. |
| [AFM-4.5B-Q5_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q5_K_L.gguf) | Q5_K_L | 3.55GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [AFM-4.5B-Q5_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q5_K_M.gguf) | Q5_K_M | 3.34GB | false | High quality, *recommended*. |
| [AFM-4.5B-Q5_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q5_K_S.gguf) | Q5_K_S | 3.23GB | false | High quality, *recommended*. |
| [AFM-4.5B-Q4_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q4_K_L.gguf) | Q4_K_L | 3.16GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [AFM-4.5B-Q4_1.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q4_1.gguf) | Q4_1 | 2.96GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [AFM-4.5B-Q4_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q4_K_M.gguf) | Q4_K_M | 2.92GB | false | Good quality, default size for most use cases, *recommended*. |
| [AFM-4.5B-Q3_K_XL.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q3_K_XL.gguf) | Q3_K_XL | 2.92GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [AFM-4.5B-Q4_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q4_K_S.gguf) | Q4_K_S | 2.72GB | false | Slightly lower quality with more space savings, *recommended*. |
| [AFM-4.5B-Q4_0.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q4_0.gguf) | Q4_0 | 2.70GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [AFM-4.5B-IQ4_NL.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ4_NL.gguf) | IQ4_NL | 2.70GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [AFM-4.5B-Q3_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q3_K_L.gguf) | Q3_K_L | 2.63GB | false | Lower quality but usable, good for low RAM availability. |
| [AFM-4.5B-IQ4_XS.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ4_XS.gguf) | IQ4_XS | 2.56GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [AFM-4.5B-Q3_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q3_K_M.gguf) | Q3_K_M | 2.40GB | false | Low quality. |
| [AFM-4.5B-Q2_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q2_K_L.gguf) | Q2_K_L | 2.21GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [AFM-4.5B-IQ3_M.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ3_M.gguf) | IQ3_M | 2.18GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [AFM-4.5B-Q3_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q3_K_S.gguf) | Q3_K_S | 2.12GB | false | Low quality, not recommended. |
| [AFM-4.5B-IQ3_XS.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ3_XS.gguf) | IQ3_XS | 2.05GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [AFM-4.5B-IQ3_XXS.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ3_XXS.gguf) | IQ3_XXS | 1.89GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [AFM-4.5B-Q2_K.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-Q2_K.gguf) | Q2_K | 1.89GB | false | Very low quality but surprisingly usable. |
| [AFM-4.5B-IQ2_M.gguf](https://huggingface.co/bartowski/arcee-ai_AFM-4.5B-GGUF/blob/main/arcee-ai_AFM-4.5B-IQ2_M.gguf) | IQ2_M | 1.70GB | 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/arcee-ai_AFM-4.5B-GGUF --include "arcee-ai_AFM-4.5B-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/arcee-ai_AFM-4.5B-GGUF --include "arcee-ai_AFM-4.5B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (arcee-ai_AFM-4.5B-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, 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
arcee-ai_AFM-4.5B-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2dc3b9cf4bb919f7f5bf81daac722c1e8012664bd4283e0111b956adb2f5069b
size 1702141600

3
arcee-ai_AFM-4.5B-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f9e4a54d724a0ece69236941e161c9d188f71b07f509fd0a4114be253a6ea913
size 2183976288

3
arcee-ai_AFM-4.5B-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:12cc34447242197af011f479a6779abf8adcd377c7abbf8c222dbf3eb0743dbb
size 2054583648

3
arcee-ai_AFM-4.5B-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d5d2cef984fb8e47fdb55fd4b719c622aa6a87f03fb748ed3f0e972fddcfc6cf
size 1893834400

3
arcee-ai_AFM-4.5B-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6c0a31c20c6fe289c85d4c0ca81529d8faef62d56c9381a832f56d7eace0ddc5
size 2697145344

3
arcee-ai_AFM-4.5B-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:21d047567dfcb92dc9b3db1ba667cb136f7908c0e1b17c6128b4720506678ca1
size 2564516480

3
arcee-ai_AFM-4.5B-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:702bb1e84615e3ef38d89f0e7e832669282b64446405cf54c884f5003af91272
size 1893015648

3
arcee-ai_AFM-4.5B-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:dd7386ab7559a59b66e7a657eb36ca908a3179ff24f857de62a69faa100124a2
size 2213028096

3
arcee-ai_AFM-4.5B-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3bbcdca8d1d8cbc9bf57b199fd5739bf87a9cb7a8a345847251795e7d4c30642
size 2632242528

3
arcee-ai_AFM-4.5B-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:07f8f198598a3ea3ddec88bd08da1eecebec949ff8904858007d2fa58b011e7b
size 2396640608

3
arcee-ai_AFM-4.5B-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:46279295723cd8fa09f36e206a2a2e0826ab4f7eb0e91c13258f7abe6b703089
size 2121307488

3
arcee-ai_AFM-4.5B-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:930dc197ccb11781d16502934ee46214e48a9a514a98492e1fcc4568c718bdf4
size 2918973696

3
arcee-ai_AFM-4.5B-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f686790a03b16d415d5dfed2f42592f8dd31d09b977358a989aac63981b3dd14
size 2703043584

3
arcee-ai_AFM-4.5B-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e36821939e87d8bd0e0dc4665871eb6599a3957f2195b8ecb421af05b66c3d8c
size 2959453984

3
arcee-ai_AFM-4.5B-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c7d3e0d4540d145665778f1437fa2db7174fbb535d70e26404e6da85ff334aa2
size 3159511296

3
arcee-ai_AFM-4.5B-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f05516b323f581bebae1af2cbf900d83a2569b0a60c54366daf4a9c15ae30d4f
size 2916301824

3
arcee-ai_AFM-4.5B-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f68b38f15070996325da741d356ca0bb62b992e4267e734cace8b800b7943e6c
size 2715495424

3
arcee-ai_AFM-4.5B-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8b966303676fe9167938635d8c22cd0f8666f01016b5d52b4a5cde00c01a9b06
size 3545846016

3
arcee-ai_AFM-4.5B-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2570a39506b6ae2f01288ddac7bc48a320f199cd6da53ae0df844f527b256e20
size 3343598144

3
arcee-ai_AFM-4.5B-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1a2aa255cb8ec65c17a4543e599bc5465a5122f35b6a637234d0cd4357c88b3e
size 3227660864

3
arcee-ai_AFM-4.5B-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1ac5833be0f529be0a056a79e6f9c47b25c536d28ced3d2732048353c7b22fdf
size 3797600512

3
arcee-ai_AFM-4.5B-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:abebad1e6ca91fedac720726e9c7ead03238fd36e42ba093968f6b1d72545725
size 3956326656

3
arcee-ai_AFM-4.5B-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2de4692fc3404ba302701753f4110610d3d8f88cf49294d5ce0f176e01cc8871
size 4916265216

3
arcee-ai_AFM-4.5B-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:fdab357e420746fc82efbcb511ca4033e4b2960db6b2622e359c4e2f58f4bea6
size 9246580128

3
arcee-ai_AFM-4.5B-imatrix.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:834d755fcf23d5197795999946c5218b2ac5e7275c07f94175bc1bef7de638ec
size 4530656

1
configuration.json Normal file
View File

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