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Updating model files

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  1. README.md +25 -2
README.md CHANGED
@@ -2,6 +2,17 @@
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  license: other
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  inference: false
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  ---
 
 
 
 
 
 
 
 
 
 
 
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  # Vicuna 7B 1.1 GPTQ 4bit 128g
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  This is a 4-bit GPTQ version of the [Vicuna 7B 1.1 model](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1).
@@ -18,7 +29,7 @@ I have the following Vicuna 1.1 repositories available:
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  * [Unquantized 13B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-13B-1.1-HF)
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  * [GPTQ quantized 4bit 13B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g)
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  * [GPTQ quantized 4bit 13B 1.1 for CPU - GGML format for `llama.cpp`](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML)
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-
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  **7B models:**
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  * [Unquantized 7B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-7B-1.1-HF)
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  * [GPTQ quantized 4bit 7B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-7B-1.1-GPTQ-4bit-128g)
@@ -73,6 +84,18 @@ Then link that into `text-generation-webui/repositories` as described above.
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  Or just use `vicuna-7B-1.1-GPTQ-4bit-128g.no-act-order.pt` as mentioned above.
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  # Vicuna Model Card
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  ## Model details
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  ## Major updates of weights v1.1
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  - Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
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- - Fix the supervised fine-tuning loss computation for better model quality.
 
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  license: other
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  inference: false
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  ---
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+ <div style="width: 100%;">
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+ <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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><a href="https://discord.gg/UBgz4VXf">Chat & support: my new Discord server</a></p>
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+ </div>
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+ <div style="display: flex; flex-direction: column; align-items: flex-end;">
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+ <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? Patreon coming soon!</a></p>
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+ </div>
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+ </div>
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  # Vicuna 7B 1.1 GPTQ 4bit 128g
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  This is a 4-bit GPTQ version of the [Vicuna 7B 1.1 model](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1).
 
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  * [Unquantized 13B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-13B-1.1-HF)
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  * [GPTQ quantized 4bit 13B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g)
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  * [GPTQ quantized 4bit 13B 1.1 for CPU - GGML format for `llama.cpp`](https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML)
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+
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  **7B models:**
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  * [Unquantized 7B 1.1 model for GPU - HF format](https://huggingface.co/TheBloke/vicuna-7B-1.1-HF)
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  * [GPTQ quantized 4bit 7B 1.1 for GPU - `safetensors` and `pt` formats](https://huggingface.co/TheBloke/vicuna-7B-1.1-GPTQ-4bit-128g)
 
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  Or just use `vicuna-7B-1.1-GPTQ-4bit-128g.no-act-order.pt` as mentioned above.
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+ ## Want to support my work?
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+
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+ I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.
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+ So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.
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+ Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.
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+ * Patreon: coming soon! (just awaiting approval)
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+ * Ko-Fi: https://ko-fi.com/TheBlokeAI
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+ * Discord: https://discord.gg/UBgz4VXf
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+
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  # Vicuna Model Card
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  ## Model details
 
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  ## Major updates of weights v1.1
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  - Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
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+ - Fix the supervised fine-tuning loss computation for better model quality.