Upload README.md

README.md

@@ -113,10 +113,12 @@ Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with T
 
 | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
 | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
-| [main](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 52.47 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
-| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 54.45 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
+| [main](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.99 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
+| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.91 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
 | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 39.64 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | 
-| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 41.52 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
+| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 41.52 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | 
+| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.87 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | 
+| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.87 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
 
 <!-- README_GPTQ.md-provided-files end -->
 
@@ -294,7 +296,8 @@ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
-prompt = "Tell me about AI"
+prompt = "Write a story about llamas"
+system_message = "You are a story writing assistant"
 prompt_template=f'''You are a helpful AI assistant.
 
 USER: {prompt}
@@ -331,7 +334,7 @@ print(pipe(prompt_template)[0]['generated_text'])
 
 The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
 
-[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
+[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
 
 For a list of clients/servers, please see "Known compatible clients / servers", above.
 <!-- README_GPTQ.md-compatibility end -->