bartowski/janhq_Jan-v1-4B-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/janhq_Jan-v1-4B-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-09-19 09:45:12 +08:00
commit 21d0bdec5a
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

164
README.md Normal file
View File

@@ -0,0 +1,164 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model_relation: quantized
base_model: janhq/Jan-v1-4B
---
## Llamacpp imatrix Quantizations of Jan-v1-4B by janhq
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b6115">b6115</a> for quantization.
Original model: https://huggingface.co/janhq/Jan-v1-4B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) combined with a subset of combined_all_small.parquet from Ed Addario [here](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_all_small.parquet)
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
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Jan-v1-4B-bf16.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-bf16.gguf) | bf16 | 8.05GB | false | Full BF16 weights. |
| [Jan-v1-4B-Q8_0.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q8_0.gguf) | Q8_0 | 4.28GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Jan-v1-4B-Q6_K_L.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q6_K_L.gguf) | Q6_K_L | 3.40GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Jan-v1-4B-Q6_K.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q6_K.gguf) | Q6_K | 3.31GB | false | Very high quality, near perfect, *recommended*. |
| [Jan-v1-4B-Q5_K_L.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q5_K_L.gguf) | Q5_K_L | 2.98GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Jan-v1-4B-Q5_K_M.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q5_K_M.gguf) | Q5_K_M | 2.89GB | false | High quality, *recommended*. |
| [Jan-v1-4B-Q5_K_S.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q5_K_S.gguf) | Q5_K_S | 2.82GB | false | High quality, *recommended*. |
| [Jan-v1-4B-Q4_1.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q4_1.gguf) | Q4_1 | 2.60GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Jan-v1-4B-Q4_K_L.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q4_K_L.gguf) | Q4_K_L | 2.59GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Jan-v1-4B-Q4_K_M.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q4_K_M.gguf) | Q4_K_M | 2.50GB | false | Good quality, default size for most use cases, *recommended*. |
| [Jan-v1-4B-Q4_K_S.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q4_K_S.gguf) | Q4_K_S | 2.38GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Jan-v1-4B-Q4_0.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q4_0.gguf) | Q4_0 | 2.38GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Jan-v1-4B-IQ4_NL.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ4_NL.gguf) | IQ4_NL | 2.38GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Jan-v1-4B-Q3_K_XL.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q3_K_XL.gguf) | Q3_K_XL | 2.33GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Jan-v1-4B-IQ4_XS.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ4_XS.gguf) | IQ4_XS | 2.27GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Jan-v1-4B-Q3_K_L.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q3_K_L.gguf) | Q3_K_L | 2.24GB | false | Lower quality but usable, good for low RAM availability. |
| [Jan-v1-4B-Q3_K_M.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q3_K_M.gguf) | Q3_K_M | 2.08GB | false | Low quality. |
| [Jan-v1-4B-IQ3_M.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ3_M.gguf) | IQ3_M | 1.96GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Jan-v1-4B-Q3_K_S.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q3_K_S.gguf) | Q3_K_S | 1.89GB | false | Low quality, not recommended. |
| [Jan-v1-4B-IQ3_XS.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ3_XS.gguf) | IQ3_XS | 1.81GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Jan-v1-4B-Q2_K_L.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q2_K_L.gguf) | Q2_K_L | 1.76GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Jan-v1-4B-IQ3_XXS.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ3_XXS.gguf) | IQ3_XXS | 1.67GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Jan-v1-4B-Q2_K.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-Q2_K.gguf) | Q2_K | 1.67GB | false | Very low quality but surprisingly usable. |
| [Jan-v1-4B-IQ2_M.gguf](https://huggingface.co/bartowski/janhq_Jan-v1-4B-GGUF/blob/main/janhq_Jan-v1-4B-IQ2_M.gguf) | IQ2_M | 1.51GB | 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/janhq_Jan-v1-4B-GGUF --include "janhq_Jan-v1-4B-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/janhq_Jan-v1-4B-GGUF --include "janhq_Jan-v1-4B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (janhq_Jan-v1-4B-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
janhq_Jan-v1-4B-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cc79ef03fa94d47448d7890406fed974f01f0fc236d42b2c7dc8fa097c3641fa
size 1512984928

3
janhq_Jan-v1-4B-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:66ba9f07f6e53b0fd5a0aa7c6c6083da81efd2e2a358afbb7dd7cfbf0850064b
size 1962897248

3
janhq_Jan-v1-4B-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b57f45c5b5816dd20a2c9d6c9d5af8f6657cc44861f91d4694c68dcefb26e464
size 1814376288

3
janhq_Jan-v1-4B-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f10459c24b0ebaf33a1d49e42c0fd531a3c478c59019c29d2e3b8eeaeacd22a5
size 1670189408

3
janhq_Jan-v1-4B-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:419e43a6494307c823032d1b92a107bf028090b39990b8e91026c9139c5e10e6
size 2381344608

3
janhq_Jan-v1-4B-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f4d5458dc212e0846315e2e0597e7352f08c5967c44250d62b0cd740772d61b4
size 2270752608

3
janhq_Jan-v1-4B-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a8f0d69156efb92b79221f8fc16e872fcfea07d3384f5bd7638cde634eac887f
size 1669500768

3
janhq_Jan-v1-4B-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e475ab2cd9ee53b15efaba12fa3629abb1750e318c9573631e9e9534cd193339
size 1763701088

3
janhq_Jan-v1-4B-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1a87cace7ee60e2e711d98c57ba4a4016755d7490af68ae0493b8f39b87018c9
size 2239786848

3
janhq_Jan-v1-4B-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0a0b1edfc84b78d21edfd0e5dc3469f8681197d450a8d704c6e182e6c84d06e5
size 2075619168

3
janhq_Jan-v1-4B-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:69adbeaea4ea21ce2df464dd610a205f82f92a6b130d11ce7e5ad5515648e6f9
size 1886998368

3
janhq_Jan-v1-4B-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:62d30e21cf648547223130fa5525f2d875011cde0a7bf07572def752a97c0eac
size 2333987168

3
janhq_Jan-v1-4B-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1f1e6ad7c8a5d8856f15b7c971064ce18275d81663e279050751eb6191405e05
size 2375774048

3
janhq_Jan-v1-4B-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d7e81c5bd42dbed10a00f92911038d3bc8e669cc76445cb66cade2d7035a415e
size 2596630368

3
janhq_Jan-v1-4B-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ba9b0b25ac7591e1d3c0f010bdc09aeb935d716e79f2763bf80076b03e19e1a0
size 2591482208

3
janhq_Jan-v1-4B-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1b9ed12488e20c4a66a248c153edef5a32d6be650041d1bd9699c6262a4eefe9
size 2497281888

3
janhq_Jan-v1-4B-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:949f8bdd99294aa0bd21886ab9929828d1c302827e8f26e7f1f9b9ac9ed1f307
size 2383310688

3
janhq_Jan-v1-4B-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:800363dd50e57de1ade0c111ef80912c0ebfcd7e4bbc2f66c0638dba1c816110
size 2983715168

3
janhq_Jan-v1-4B-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2d4a9f1fc318d13eec20ba35d21fce4e04fb3470a781baa8964846c6774b398e
size 2889514848

3
janhq_Jan-v1-4B-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c3fd1257c7deab2e8a546512881b101a31678a9e34d39030a949a3d621a79755
size 2823712608

3
janhq_Jan-v1-4B-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:31c58b042dd4b3f99354dff2560d879525db1aa01420133f067a34e3c3e288f9
size 3306262368

3
janhq_Jan-v1-4B-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ec08591dff2ed9143ca577ad44422848db08d57b97ada78d9a1986805b04831b
size 3400462688

3
janhq_Jan-v1-4B-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:53ac3c1b4911681a8d25468a7e903249e89838355a05de9ebee305fde296db53
size 4280406368

3
janhq_Jan-v1-4B-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5568e217ff22e471849ac973789ad8f9a7008e020525783de329687bb6714aee
size 8051286080

3
janhq_Jan-v1-4B-imatrix.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:33af42ebe6e3f6c5bc3b2649e2f4f0b8c8b9d939c0a141beaa2f764e9f3934fe
size 3872640