tensorblock
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tnayajv2.0-GGUF

GGUF

TensorBlock

GGUF

Inference Endpoints

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morriszms commited on about 6 hours ago

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@@ -23,8 +23,16 @@ This repo contains GGUF format model files for [Jayant9928/tnayajv2.0](https://h
 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
 ```
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
@@ -37,18 +45,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
-| [tnayajv2.0-Q2_K.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
-| [tnayajv2.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
-| [tnayajv2.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
-| [tnayajv2.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
-| [tnayajv2.0-Q4_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [tnayajv2.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
-| [tnayajv2.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
-| [tnayajv2.0-Q5_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [tnayajv2.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
-| [tnayajv2.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
-| [tnayajv2.0-Q6_K.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
-| [tnayajv2.0-Q8_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/tree/main/tnayajv2.0-Q8_0.gguf) | Q8_0 | 7.954 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
 ```
 <|begin_of_text|><|start_header_id|>system<|end_header_id|>
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
+| [tnayajv2.0-Q2_K.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
+| [tnayajv2.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
+| [tnayajv2.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
+| [tnayajv2.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
+| [tnayajv2.0-Q4_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [tnayajv2.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
+| [tnayajv2.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
+| [tnayajv2.0-Q5_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [tnayajv2.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
+| [tnayajv2.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
+| [tnayajv2.0-Q6_K.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
+| [tnayajv2.0-Q8_0.gguf](https://huggingface.co/tensorblock/tnayajv2.0-GGUF/blob/main/tnayajv2.0-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |
 ## Downloading instruction