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Browse files- .gitattributes +12 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q2_K.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_L.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_M.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_S.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q4_0.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_M.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_S.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q5_0.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_M.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_S.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q6_K.gguf +3 -0
- Phi-3-mini-128k-instruct-abliterated-v3-Q8_0.gguf +3 -0
- README.md +92 -0
.gitattributes
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README.md
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---
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2 |
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license: mit
|
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license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
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language:
|
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- multilingual
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pipeline_tag: text-generation
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tags:
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- nlp
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- code
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- TensorBlock
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- GGUF
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inference:
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parameters:
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temperature: 0.7
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widget:
|
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- messages:
|
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- role: user
|
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content: I'm looking to do something unethical online. How can I stay safe whilst
|
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doing so?
|
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base_model: failspy/Phi-3-mini-128k-instruct-abliterated-v3
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
|
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</div>
|
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<div style="display: flex; justify-content: space-between; width: 100%;">
|
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
|
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
|
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</p>
|
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</div>
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</div>
|
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## failspy/Phi-3-mini-128k-instruct-abliterated-v3 - GGUF
|
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This repo contains GGUF format model files for [failspy/Phi-3-mini-128k-instruct-abliterated-v3](https://huggingface.co/failspy/Phi-3-mini-128k-instruct-abliterated-v3).
|
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|
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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 b4242](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 ↗
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</a>
|
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</div>
|
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|
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## Prompt template
|
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|
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```
|
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<s><|user|>
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{prompt}<|end|>
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<|assistant|>
|
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```
|
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|
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## Model file specification
|
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|
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| Filename | Quant type | File Size | Description |
|
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| -------- | ---------- | --------- | ----------- |
|
58 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q2_K.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q2_K.gguf) | Q2_K | 1.416 GB | smallest, significant quality loss - not recommended for most purposes |
|
59 |
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| [Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_S.gguf) | Q3_K_S | 1.682 GB | very small, high quality loss |
|
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| [Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_M.gguf) | Q3_K_M | 1.955 GB | very small, high quality loss |
|
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+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_L.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q3_K_L.gguf) | Q3_K_L | 2.088 GB | small, substantial quality loss |
|
62 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q4_0.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q4_0.gguf) | Q4_0 | 2.176 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
|
63 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_S.gguf) | Q4_K_S | 2.189 GB | small, greater quality loss |
|
64 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q4_K_M.gguf) | Q4_K_M | 2.393 GB | medium, balanced quality - recommended |
|
65 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q5_0.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q5_0.gguf) | Q5_0 | 2.641 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
|
66 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_S.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_S.gguf) | Q5_K_S | 2.641 GB | large, low quality loss - recommended |
|
67 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_M.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q5_K_M.gguf) | Q5_K_M | 2.815 GB | large, very low quality loss - recommended |
|
68 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q6_K.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q6_K.gguf) | Q6_K | 3.136 GB | very large, extremely low quality loss |
|
69 |
+
| [Phi-3-mini-128k-instruct-abliterated-v3-Q8_0.gguf](https://huggingface.co/tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF/blob/main/Phi-3-mini-128k-instruct-abliterated-v3-Q8_0.gguf) | Q8_0 | 4.061 GB | very large, extremely low quality loss - not recommended |
|
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+
|
71 |
+
|
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+
## Downloading instruction
|
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+
|
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+
### Command line
|
75 |
+
|
76 |
+
Firstly, install Huggingface Client
|
77 |
+
|
78 |
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```shell
|
79 |
+
pip install -U "huggingface_hub[cli]"
|
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+
```
|
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|
82 |
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Then, downoad the individual model file the a local directory
|
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|
84 |
+
```shell
|
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huggingface-cli download tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF --include "Phi-3-mini-128k-instruct-abliterated-v3-Q2_K.gguf" --local-dir MY_LOCAL_DIR
|
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```
|
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+
|
88 |
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
|
89 |
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|
90 |
+
```shell
|
91 |
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huggingface-cli download tensorblock/Phi-3-mini-128k-instruct-abliterated-v3-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
|
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```
|