Upload folder using huggingface_hub

Llama-3-8B-Q2_K.gguf

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Llama-3-8B-Q3_K_L.gguf

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Llama-3-8B-Q8_0.gguf

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README.md

@@ -1,16 +1,21 @@
 ---
 language:
-- en
-library_name: transformers
+- sv
+- da
+- 'no'
 license: llama3
 tags:
-- unsloth
-- transformers
+- pytorch
 - llama
 - llama-3
+- ai-sweden
 - TensorBlock
 - GGUF
-base_model: unsloth/llama-3-8b
+base_model: AI-Sweden-Models/Llama-3-8B
+pipeline_tag: text-generation
+inference:
+  parameters:
+    temperature: 0.6
 ---
 
 <div style="width: auto; margin-left: auto; margin-right: auto">
@@ -24,12 +29,11 @@ base_model: unsloth/llama-3-8b
     </div>
 </div>
 
-## unsloth/llama-3-8b - GGUF
+## AI-Sweden-Models/Llama-3-8B - GGUF
 
-This repo contains GGUF format model files for [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b).
-
-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).
+This repo contains GGUF format model files for [AI-Sweden-Models/Llama-3-8B](https://huggingface.co/AI-Sweden-Models/Llama-3-8B).
 
+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;">
@@ -39,7 +43,6 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 
 ## Prompt template
 
-
 ```
 
 ```
@@ -48,18 +51,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
-| [llama-3-8b-Q2_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
-| [llama-3-8b-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
-| [llama-3-8b-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
-| [llama-3-8b-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
-| [llama-3-8b-Q4_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [llama-3-8b-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
-| [llama-3-8b-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
-| [llama-3-8b-Q5_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [llama-3-8b-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
-| [llama-3-8b-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
-| [llama-3-8b-Q6_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
-| [llama-3-8b-Q8_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-GGUF/blob/main/llama-3-8b-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |
+| [Llama-3-8B-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
+| [Llama-3-8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q3_K_S.gguf) | Q3_K_S | 3.664 GB | very small, high quality loss |
+| [Llama-3-8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
+| [Llama-3-8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
+| [Llama-3-8B-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Llama-3-8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
+| [Llama-3-8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
+| [Llama-3-8B-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Llama-3-8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
+| [Llama-3-8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
+| [Llama-3-8B-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
+| [Llama-3-8B-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-3-8B-GGUF/blob/main/Llama-3-8B-Q8_0.gguf) | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
 
 
 ## Downloading instruction
@@ -75,11 +78,11 @@ pip install -U "huggingface_hub[cli]"
 Then, downoad the individual model file the a local directory
 
 ```shell
-huggingface-cli download tensorblock/llama-3-8b-GGUF --include "llama-3-8b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
+huggingface-cli download tensorblock/Llama-3-8B-GGUF --include "Llama-3-8B-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/llama-3-8b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
+huggingface-cli download tensorblock/Llama-3-8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
 ```