初始化项目，由ModelHub XC社区提供模型

Model: bartowski/tencent_Hunyuan-7B-Instruct-GGUF
Source: Original Platform

.gitattributes

@@ -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

README.md

@@ -0,0 +1,166 @@
+---
+quantized_by: bartowski
+pipeline_tag: text-generation
+base_model_relation: quantized
+base_model: tencent/Hunyuan-7B-Instruct
+---
+
+## Llamacpp imatrix Quantizations of Hunyuan-7B-Instruct by tencent
+
+Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b6076">b6076</a> for quantization.
+
+Original model: https://huggingface.co/tencent/Hunyuan-7B-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/)
+
+Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
+
+## Prompt format
+
+```
+<|startoftext|>{system_prompt}<|extra_4|>{prompt}<|extra_0|><|eos|><|extra_0|>
+```
+
+## Download a file (not the whole branch) from below:
+
+| Filename | Quant type | File Size | Split | Description |
+| -------- | ---------- | --------- | ----- | ----------- |
+| [Hunyuan-7B-Instruct-bf16.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-bf16.gguf) | bf16 | 15.01GB | false | Full BF16 weights. |
+| [Hunyuan-7B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q8_0.gguf) | Q8_0 | 7.98GB | false | Extremely high quality, generally unneeded but max available quant. |
+| [Hunyuan-7B-Instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q6_K_L.gguf) | Q6_K_L | 6.29GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
+| [Hunyuan-7B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q6_K.gguf) | Q6_K | 6.16GB | false | Very high quality, near perfect, *recommended*. |
+| [Hunyuan-7B-Instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q5_K_L.gguf) | Q5_K_L | 5.50GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
+| [Hunyuan-7B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.37GB | false | High quality, *recommended*. |
+| [Hunyuan-7B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.23GB | false | High quality, *recommended*. |
+| [Hunyuan-7B-Instruct-Q4_1.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q4_1.gguf) | Q4_1 | 4.80GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
+| [Hunyuan-7B-Instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q4_K_L.gguf) | Q4_K_L | 4.75GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
+| [Hunyuan-7B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.62GB | false | Good quality, default size for most use cases, *recommended*. |
+| [Hunyuan-7B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.39GB | false | Slightly lower quality with more space savings, *recommended*. |
+| [Hunyuan-7B-Instruct-Q4_0.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q4_0.gguf) | Q4_0 | 4.38GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
+| [Hunyuan-7B-Instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ4_NL.gguf) | IQ4_NL | 4.38GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
+| [Hunyuan-7B-Instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q3_K_XL.gguf) | Q3_K_XL | 4.22GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
+| [Hunyuan-7B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.17GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
+| [Hunyuan-7B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.09GB | false | Lower quality but usable, good for low RAM availability. |
+| [Hunyuan-7B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3.79GB | false | Low quality. |
+| [Hunyuan-7B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ3_M.gguf) | IQ3_M | 3.56GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
+| [Hunyuan-7B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.44GB | false | Low quality, not recommended. |
+| [Hunyuan-7B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.29GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
+| [Hunyuan-7B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q2_K_L.gguf) | Q2_K_L | 3.13GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [Hunyuan-7B-Instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ3_XXS.gguf) | IQ3_XXS | 3.05GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
+| [Hunyuan-7B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-Q2_K.gguf) | Q2_K | 3.00GB | false | Very low quality but surprisingly usable. |
+| [Hunyuan-7B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF/blob/main/tencent_Hunyuan-7B-Instruct-IQ2_M.gguf) | IQ2_M | 2.72GB | 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/tencent_Hunyuan-7B-Instruct-GGUF --include "tencent_Hunyuan-7B-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/tencent_Hunyuan-7B-Instruct-GGUF --include "tencent_Hunyuan-7B-Instruct-Q8_0/*" --local-dir ./
+```
+
+You can either specify a new local-dir (tencent_Hunyuan-7B-Instruct-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

configuration.json

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

tencent_Hunyuan-7B-Instruct-IQ2_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30e48d3f9ba2343e239bac4179fceb21420cad4740d68420f390afd59bca56ce
+size 2719359264

tencent_Hunyuan-7B-Instruct-IQ3_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b83c7ab828df908de624567af631c8a4db0b5acb84d7cab56e9871579efb8013
+size 3555853312

tencent_Hunyuan-7B-Instruct-IQ3_XS.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2be35d983723441e2f5692f06794b915029684a4a6a11ed008cb369d9a771cdd
+size 3289777152

tencent_Hunyuan-7B-Instruct-IQ3_XXS.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42cd5528d789bf252fa93b9f7866c9708df816ffc93612b2a6f1d6e11efe1322
+size 3045990688

tencent_Hunyuan-7B-Instruct-IQ4_NL.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90d4ef6404356f3a7e0c0fcdf523c0b8105bb7e5646a1118b3f598ee107de4ef
+size 4379247616

tencent_Hunyuan-7B-Instruct-IQ4_XS.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ebad9f23d8bbf7ac3af4e18b0b3022b17284d967a725c3b9b3ebc133f686e08
+size 4165338112

tencent_Hunyuan-7B-Instruct-Q2_K.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea9921f4107e708ec7ae60182ca31214a4d468d9445743512983ff14990a550f
+size 3003515904

tencent_Hunyuan-7B-Instruct-Q2_K_L.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0057ad02fb7140899b58af7162b71e5b2d8afb6dbe2e209ad92b620606e73af9
+size 3130657568

tencent_Hunyuan-7B-Instruct-Q3_K_L.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09b13843a066ce7839aa524218063b718cb9e2cbeb9b98cc0a6edb6d6bf23bad
+size 4092986368

tencent_Hunyuan-7B-Instruct-Q3_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50984a7f343f492776bc4900380021785d33b8c7baeb5bf98beb46ef27f4ee87
+size 3789947904

tencent_Hunyuan-7B-Instruct-Q3_K_S.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:112d1813d17f29f7786e8bd181f7fb77756cf0494388586bec6d91e967cc72d4
+size 3435529216

tencent_Hunyuan-7B-Instruct-Q3_K_XL.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a160e4d540eba08d7d34980619df79e26fbbc493184e79beec4294c2bfae35a3
+size 4220128032

tencent_Hunyuan-7B-Instruct-Q4_0.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:289187a6ae4ff7482c61bc1d2c165d97cf74ca85d1bb63ce2d9aa46354ef93ac
+size 4377150464

tencent_Hunyuan-7B-Instruct-Q4_1.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:123dcf760de41854e90d52ad823b2208da20b3b67344b329a93b72db878276f8
+size 4798678016

tencent_Hunyuan-7B-Instruct-Q4_K_L.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3416d123c6464e59a9bd5d92a71b89f6265bf9dc4922b3f2a918d078158a0889
+size 4749134624

tencent_Hunyuan-7B-Instruct-Q4_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37361ed44b126de00a551260fd7c3de3c3a7a58d6060bce99b6b8fe0c0665d63
+size 4621992960

tencent_Hunyuan-7B-Instruct-Q4_K_S.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cea88d81a3d0b690e39eda7326c5d992398c496814a6291c471d9b473a5c2312
+size 4393927680

tencent_Hunyuan-7B-Instruct-Q5_K_L.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6a43b537b0d857ce266387020c5ac47868912105c6e016d519de997a21ad797
+size 5495720736

tencent_Hunyuan-7B-Instruct-Q5_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb253866770662c79b52238cd76b6682c80c1abedb06c6d22a84ee31949c7e1b
+size 5368579072

tencent_Hunyuan-7B-Instruct-Q5_K_S.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34207625291f3255000b4faa5a8b11a82f860e109374fd83c4255b4aae570f1f
+size 5234885632

tencent_Hunyuan-7B-Instruct-Q6_K.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bd4185982694560988e5f684e0e1d2f0743e6addc002ce12ef1e1a34e962d5c
+size 6161826816

tencent_Hunyuan-7B-Instruct-Q6_K_L.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21f08d206e48645c091fc99231e8b4a218cdd93103d09049b76115fb2136a275
+size 6288968480

tencent_Hunyuan-7B-Instruct-Q8_0.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:815b1d7a7528d5857c427bcfda08e4919458b7d29787243d58d2eb9dc90d51b7
+size 7979272992

tencent_Hunyuan-7B-Instruct-bf16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:401e9be1527797bbbea1af39709684a8d2cf1b5f4b80fae8129bd93db2ce1124
+size 15014548224

tencent_Hunyuan-7B-Instruct-imatrix.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1580f854945695e5edc0a5314c061c3846925b5b405c4bfde5b9a3cdf11b460c
+size 5015232