Update README.md

README.md

@@ -66,10 +66,10 @@ Each separate quant is in a different branch.  See below for instructions on fet
 
 | Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
 | ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
-| main | 4 | 128 | False | 4.21 GB | True | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
-| gptq-4bit-32g-actorder_True | 4 | 32 | True | 4.59 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-64g-actorder_True | 4 | 64 | True | 4.34 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-128g-actorder_True | 4 | 128 | True | 4.21 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| main | 4 | 128 | False | 4.21 GB | False | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
+| gptq-4bit-32g-actorder_True | 4 | 32 | True | 4.59 GB | False | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-64g-actorder_True | 4 | 64 | True | 4.34 GB | False | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
+| gptq-4bit-128g-actorder_True | 4 | 128 | True | 4.21 GB | False | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
 | gptq-8bit--1g-actorder_True | 8 | None | True | 7.33 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
 | gptq-8bit-128g-actorder_False | 8 | 128 | False | 7.47 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
 
@@ -88,6 +88,10 @@ Please make sure you're using the latest version of [text-generation-webui](http
 
 It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
 
+Please remember to install `tiktoken`, as listed above.
+
+You must also tick "Trust Remote Code", which means it's not compatible with ExLlama.
+
 1. Click the **Model tab**.
 2. Under **Download custom model or LoRA**, enter `TheBloke/Codegen25-7B-mono-GPTQ`.
   - To download from a specific branch, enter for example `TheBloke/Codegen25-7B-mono-GPTQ:gptq-4bit-32g-actorder_True`
@@ -95,11 +99,12 @@ It is strongly recommended to use the text-generation-webui one-click-installers
 3. Click **Download**.
 4. The model will start downloading. Once it's finished it will say "Done"
 5. In the top left, click the refresh icon next to **Model**.
-6. In the **Model** dropdown, choose the model you just downloaded: `Codegen25-7B-mono-GPTQ`
-7. The model will automatically load, and is now ready for use!
-8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
+6. Make sure Loader is set to AutoGPTQ or GPTQ-for-LLaMa, and that Trust Remote Code is ticked.
+7. In the **Model** dropdown, choose the model you just downloaded: `Codegen25-7B-mono-GPTQ`
+8. The model will automatically load, and is now ready for use!
+9. To save your settings (Loader and Trust Remote Code), click **Save settings for this model** followed by **Reload the Model** in the top right.
   * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
-9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
+10. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
 
 ## How to use this GPTQ model from Python code