Upload folder using huggingface_hub

CodeGemma-7b-Q2_K.gguf

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CodeGemma-7b-Q3_K_L.gguf

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CodeGemma-7b-Q3_K_M.gguf

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CodeGemma-7b-Q3_K_S.gguf

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CodeGemma-7b-Q4_0.gguf

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CodeGemma-7b-Q4_K_M.gguf

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CodeGemma-7b-Q4_K_S.gguf

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CodeGemma-7b-Q5_0.gguf

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CodeGemma-7b-Q5_K_M.gguf

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CodeGemma-7b-Q5_K_S.gguf

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CodeGemma-7b-Q6_K.gguf

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CodeGemma-7b-Q8_0.gguf

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README.md

@@ -1,16 +1,15 @@
 ---
-library_name: transformers
-license: gemma
-license_link: https://ai.google.dev/gemma/terms
-extra_gated_heading: Access CodeGemma on Hugging Face
-extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
-  and agree to Google’s usage license. To do this, please ensure you’re logged-in
-  to Hugging Face and click below. Requests are processed immediately.
-extra_gated_button_content: Acknowledge license
-base_model: google/codegemma-7b
 tags:
+- code
+- gemma
 - TensorBlock
 - GGUF
+library_name: transformers
+pipeline_tag: text-generation
+license: other
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+base_model: TechxGenus/CodeGemma-7b
 ---
 
 <div style="width: auto; margin-left: auto; margin-right: auto">
@@ -24,11 +23,11 @@ tags:
     </div>
 </div>
 
-## google/codegemma-7b - GGUF
+## TechxGenus/CodeGemma-7b - GGUF
 
-This repo contains GGUF format model files for [google/codegemma-7b](https://huggingface.co/google/codegemma-7b).
+This repo contains GGUF format model files for [TechxGenus/CodeGemma-7b](https://huggingface.co/TechxGenus/CodeGemma-7b).
 
-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).
+The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](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;">
@@ -46,18 +45,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
-| [codegemma-7b-Q2_K.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q2_K.gguf) | Q2_K | 3.481 GB | smallest, significant quality loss - not recommended for most purposes |
-| [codegemma-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q3_K_S.gguf) | Q3_K_S | 3.982 GB | very small, high quality loss |
-| [codegemma-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q3_K_M.gguf) | Q3_K_M | 4.369 GB | very small, high quality loss |
-| [codegemma-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q3_K_L.gguf) | Q3_K_L | 4.709 GB | small, substantial quality loss |
-| [codegemma-7b-Q4_0.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q4_0.gguf) | Q4_0 | 5.012 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [codegemma-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q4_K_S.gguf) | Q4_K_S | 5.046 GB | small, greater quality loss |
-| [codegemma-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q4_K_M.gguf) | Q4_K_M | 5.330 GB | medium, balanced quality - recommended |
-| [codegemma-7b-Q5_0.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q5_0.gguf) | Q5_0 | 5.981 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [codegemma-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q5_K_S.gguf) | Q5_K_S | 5.981 GB | large, low quality loss - recommended |
-| [codegemma-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q5_K_M.gguf) | Q5_K_M | 6.145 GB | large, very low quality loss - recommended |
-| [codegemma-7b-Q6_K.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q6_K.gguf) | Q6_K | 7.010 GB | very large, extremely low quality loss |
-| [codegemma-7b-Q8_0.gguf](https://huggingface.co/tensorblock/codegemma-7b-GGUF/blob/main/codegemma-7b-Q8_0.gguf) | Q8_0 | 9.078 GB | very large, extremely low quality loss - not recommended |
+| [CodeGemma-7b-Q2_K.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q2_K.gguf) | Q2_K | 3.481 GB | smallest, significant quality loss - not recommended for most purposes |
+| [CodeGemma-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_S.gguf) | Q3_K_S | 3.982 GB | very small, high quality loss |
+| [CodeGemma-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_M.gguf) | Q3_K_M | 4.369 GB | very small, high quality loss |
+| [CodeGemma-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_L.gguf) | Q3_K_L | 4.709 GB | small, substantial quality loss |
+| [CodeGemma-7b-Q4_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_0.gguf) | Q4_0 | 5.012 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [CodeGemma-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_K_S.gguf) | Q4_K_S | 5.046 GB | small, greater quality loss |
+| [CodeGemma-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_K_M.gguf) | Q4_K_M | 5.330 GB | medium, balanced quality - recommended |
+| [CodeGemma-7b-Q5_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_0.gguf) | Q5_0 | 5.981 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [CodeGemma-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_K_S.gguf) | Q5_K_S | 5.981 GB | large, low quality loss - recommended |
+| [CodeGemma-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_K_M.gguf) | Q5_K_M | 6.145 GB | large, very low quality loss - recommended |
+| [CodeGemma-7b-Q6_K.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q6_K.gguf) | Q6_K | 7.010 GB | very large, extremely low quality loss |
+| [CodeGemma-7b-Q8_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q8_0.gguf) | Q8_0 | 9.078 GB | very large, extremely low quality loss - not recommended |
 
 
 ## Downloading instruction
@@ -73,11 +72,11 @@ pip install -U "huggingface_hub[cli]"
 Then, downoad the individual model file the a local directory
 
 ```shell
-huggingface-cli download tensorblock/codegemma-7b-GGUF --include "codegemma-7b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
+huggingface-cli download tensorblock/CodeGemma-7b-GGUF --include "CodeGemma-7b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
 ```
 
 If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
 
 ```shell
-huggingface-cli download tensorblock/codegemma-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+huggingface-cli download tensorblock/CodeGemma-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
 ```