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codegemma-2b-GGUF

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  1. README.md +22 -24
  2. codegemma-2b-Q2_K.gguf +2 -2
  3. codegemma-2b-Q3_K_L.gguf +2 -2
  4. codegemma-2b-Q3_K_M.gguf +2 -2
  5. codegemma-2b-Q3_K_S.gguf +2 -2
  6. codegemma-2b-Q4_0.gguf +2 -2
  7. codegemma-2b-Q4_K_M.gguf +2 -2
  8. codegemma-2b-Q4_K_S.gguf +2 -2
  9. codegemma-2b-Q5_0.gguf +2 -2
  10. codegemma-2b-Q5_K_M.gguf +2 -2
  11. codegemma-2b-Q5_K_S.gguf +2 -2
  12. codegemma-2b-Q6_K.gguf +2 -2
  13. codegemma-2b-Q8_0.gguf +2 -2
README.md CHANGED
@@ -1,16 +1,16 @@
1
  ---
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- language:
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- - en
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  library_name: transformers
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- license: apache-2.0
 
 
 
 
 
 
 
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  tags:
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- - unsloth
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- - transformers
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- - gemma
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- - bnb
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  - TensorBlock
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  - GGUF
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- base_model: unsloth/codegemma-2b
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  ---
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  <div style="width: auto; margin-left: auto; margin-right: auto">
@@ -24,13 +24,12 @@ base_model: unsloth/codegemma-2b
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  </div>
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  </div>
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- ## unsloth/codegemma-2b - GGUF
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- This repo contains GGUF format model files for [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b).
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  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).
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-
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  <div style="text-align: left; margin: 20px 0;">
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  <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;">
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  Run them on the TensorBlock client using your local machine ↗
@@ -39,7 +38,6 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
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  ## Prompt template
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-
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  ```
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  ```
@@ -48,18 +46,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
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  | Filename | Quant type | File Size | Description |
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  | -------- | ---------- | --------- | ----------- |
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- | [codegemma-2b-Q2_K.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q2_K.gguf) | Q2_K | 1.078 GB | smallest, significant quality loss - not recommended for most purposes |
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- | [codegemma-2b-Q3_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_S.gguf) | Q3_K_S | 1.200 GB | very small, high quality loss |
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- | [codegemma-2b-Q3_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_M.gguf) | Q3_K_M | 1.289 GB | very small, high quality loss |
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- | [codegemma-2b-Q3_K_L.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_L.gguf) | Q3_K_L | 1.365 GB | small, substantial quality loss |
55
- | [codegemma-2b-Q4_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_0.gguf) | Q4_0 | 1.445 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
56
- | [codegemma-2b-Q4_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_K_S.gguf) | Q4_K_S | 1.453 GB | small, greater quality loss |
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- | [codegemma-2b-Q4_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_K_M.gguf) | Q4_K_M | 1.518 GB | medium, balanced quality - recommended |
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- | [codegemma-2b-Q5_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_0.gguf) | Q5_0 | 1.675 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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- | [codegemma-2b-Q5_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_K_S.gguf) | Q5_K_S | 1.675 GB | large, low quality loss - recommended |
60
- | [codegemma-2b-Q5_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_K_M.gguf) | Q5_K_M | 1.713 GB | large, very low quality loss - recommended |
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- | [codegemma-2b-Q6_K.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q6_K.gguf) | Q6_K | 1.921 GB | very large, extremely low quality loss |
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- | [codegemma-2b-Q8_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q8_0.gguf) | Q8_0 | 2.486 GB | very large, extremely low quality loss - not recommended |
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  ## Downloading instruction
 
1
  ---
 
 
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  library_name: transformers
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+ license: gemma
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+ license_link: https://ai.google.dev/gemma/terms
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+ extra_gated_heading: Access CodeGemma on Hugging Face
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+ extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
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+ and agree to Google’s usage license. To do this, please ensure you’re logged-in
8
+ to Hugging Face and click below. Requests are processed immediately.
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+ extra_gated_button_content: Acknowledge license
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+ base_model: google/codegemma-2b
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  tags:
 
 
 
 
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  - TensorBlock
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  - GGUF
 
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  ---
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  <div style="width: auto; margin-left: auto; margin-right: auto">
 
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  </div>
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  </div>
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+ ## google/codegemma-2b - GGUF
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+ This repo contains GGUF format model files for [google/codegemma-2b](https://huggingface.co/google/codegemma-2b).
30
 
31
  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).
32
 
 
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  <div style="text-align: left; margin: 20px 0;">
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  <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;">
35
  Run them on the TensorBlock client using your local machine ↗
 
38
 
39
  ## Prompt template
40
 
 
41
  ```
42
 
43
  ```
 
46
 
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  | Filename | Quant type | File Size | Description |
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  | -------- | ---------- | --------- | ----------- |
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+ | [codegemma-2b-Q2_K.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q2_K.gguf) | Q2_K | 1.158 GB | smallest, significant quality loss - not recommended for most purposes |
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+ | [codegemma-2b-Q3_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_S.gguf) | Q3_K_S | 1.288 GB | very small, high quality loss |
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+ | [codegemma-2b-Q3_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_M.gguf) | Q3_K_M | 1.384 GB | very small, high quality loss |
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+ | [codegemma-2b-Q3_K_L.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q3_K_L.gguf) | Q3_K_L | 1.466 GB | small, substantial quality loss |
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+ | [codegemma-2b-Q4_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_0.gguf) | Q4_0 | 1.551 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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+ | [codegemma-2b-Q4_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_K_S.gguf) | Q4_K_S | 1.560 GB | small, greater quality loss |
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+ | [codegemma-2b-Q4_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q4_K_M.gguf) | Q4_K_M | 1.630 GB | medium, balanced quality - recommended |
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+ | [codegemma-2b-Q5_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_0.gguf) | Q5_0 | 1.799 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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+ | [codegemma-2b-Q5_K_S.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_K_S.gguf) | Q5_K_S | 1.799 GB | large, low quality loss - recommended |
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+ | [codegemma-2b-Q5_K_M.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q5_K_M.gguf) | Q5_K_M | 1.840 GB | large, very low quality loss - recommended |
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+ | [codegemma-2b-Q6_K.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q6_K.gguf) | Q6_K | 2.062 GB | very large, extremely low quality loss |
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+ | [codegemma-2b-Q8_0.gguf](https://huggingface.co/tensorblock/codegemma-2b-GGUF/blob/main/codegemma-2b-Q8_0.gguf) | Q8_0 | 2.669 GB | very large, extremely low quality loss - not recommended |
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  ## Downloading instruction
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