tensorblock
/

Yi-9B-200K-GGUF

Text Generation

GGUF

TensorBlock

GGUF

Inference Endpoints

Model card Files Files and versions Community

morriszms commited on Nov 16, 2024

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@@ -48,8 +48,16 @@ This repo contains GGUF format model files for [01-ai/Yi-9B-200K](https://huggin
 The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
 ## Prompt template
 ```
 ```
@@ -58,18 +66,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
-| [Yi-9B-200K-Q2_K.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q2_K.gguf) | Q2_K | 3.124 GB | smallest, significant quality loss - not recommended for most purposes |
-| [Yi-9B-200K-Q3_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q3_K_S.gguf) | Q3_K_S | 3.631 GB | very small, high quality loss |
-| [Yi-9B-200K-Q3_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q3_K_M.gguf) | Q3_K_M | 4.027 GB | very small, high quality loss |
-| [Yi-9B-200K-Q3_K_L.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q3_K_L.gguf) | Q3_K_L | 4.369 GB | small, substantial quality loss |
-| [Yi-9B-200K-Q4_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q4_0.gguf) | Q4_0 | 4.691 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Yi-9B-200K-Q4_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q4_K_S.gguf) | Q4_K_S | 4.724 GB | small, greater quality loss |
-| [Yi-9B-200K-Q4_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q4_K_M.gguf) | Q4_K_M | 4.963 GB | medium, balanced quality - recommended |
-| [Yi-9B-200K-Q5_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q5_0.gguf) | Q5_0 | 5.688 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [Yi-9B-200K-Q5_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q5_K_S.gguf) | Q5_K_S | 5.688 GB | large, low quality loss - recommended |
-| [Yi-9B-200K-Q5_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q5_K_M.gguf) | Q5_K_M | 5.828 GB | large, very low quality loss - recommended |
-| [Yi-9B-200K-Q6_K.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q6_K.gguf) | Q6_K | 6.748 GB | very large, extremely low quality loss |
-| [Yi-9B-200K-Q8_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/tree/main/Yi-9B-200K-Q8_0.gguf) | Q8_0 | 8.739 GB | very large, extremely low quality loss - not recommended |
 ## Downloading instruction

 The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
+<div style="text-align: left; margin: 20px 0;">
+    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
+        Run them on the TensorBlock client using your local machine ↗
+    </a>
+</div>
 ## Prompt template
 ```
 ```
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
+| [Yi-9B-200K-Q2_K.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q2_K.gguf) | Q2_K | 3.124 GB | smallest, significant quality loss - not recommended for most purposes |
+| [Yi-9B-200K-Q3_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q3_K_S.gguf) | Q3_K_S | 3.631 GB | very small, high quality loss |
+| [Yi-9B-200K-Q3_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q3_K_M.gguf) | Q3_K_M | 4.027 GB | very small, high quality loss |
+| [Yi-9B-200K-Q3_K_L.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q3_K_L.gguf) | Q3_K_L | 4.369 GB | small, substantial quality loss |
+| [Yi-9B-200K-Q4_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q4_0.gguf) | Q4_0 | 4.691 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Yi-9B-200K-Q4_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q4_K_S.gguf) | Q4_K_S | 4.724 GB | small, greater quality loss |
+| [Yi-9B-200K-Q4_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q4_K_M.gguf) | Q4_K_M | 4.963 GB | medium, balanced quality - recommended |
+| [Yi-9B-200K-Q5_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q5_0.gguf) | Q5_0 | 5.688 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Yi-9B-200K-Q5_K_S.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q5_K_S.gguf) | Q5_K_S | 5.688 GB | large, low quality loss - recommended |
+| [Yi-9B-200K-Q5_K_M.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q5_K_M.gguf) | Q5_K_M | 5.828 GB | large, very low quality loss - recommended |
+| [Yi-9B-200K-Q6_K.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q6_K.gguf) | Q6_K | 6.748 GB | very large, extremely low quality loss |
+| [Yi-9B-200K-Q8_0.gguf](https://huggingface.co/tensorblock/Yi-9B-200K-GGUF/blob/main/Yi-9B-200K-Q8_0.gguf) | Q8_0 | 8.739 GB | very large, extremely low quality loss - not recommended |
 ## Downloading instruction