addressing new comments after merge

Signed-off-by: Matt Williams <m@technovangelist.com>

docs/modelfile.md

@@ -124,7 +124,7 @@ PARAMETER <parameter> <parametervalue>
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | int        | repeat_last_n 64     |
 | repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                     | float      | repeat_penalty 1.1   |
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
-| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
+| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. | int | seed 42 |
 | stop           | Sets the stop sequences to use.                                                                                                                                                                                                                         | string     | stop "AI assistant:" |
 | tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1              |
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |

docs/quantize.md

@@ -1,6 +1,6 @@
 # How to Quantize a Model
 
-Sometimes the model you want to work with is not available at [https://ollama.ai/library](https://ollama.ai/library). If you want to try out that model before we have a chance to quantize it, you can use this process.
+Sometimes the model you want to work with is not available at [https://ollama.ai/library](https://ollama.ai/library).
 
 ## Figure out if we can run the model?
 
@@ -37,6 +37,20 @@ This will output two files into the directory. First is a f16.bin file that is t
 
 You can find the repository for the Docker container here: [https://github.com/mxyng/quantize](https://github.com/mxyng/quantize)
 
+For instance, if you wanted to convert the Mistral 7B model to a Q4 quantized model, then you could go through the following steps:
+
+1. First verify the model will potentially work.
+2. Now clone Mistral 7B to your machine. You can find the command to run when you click the three vertical dots button on the model page, then click **Clone Repository**.
+   1. For this repo, the command is:
+
+      ```shell
+      git lfs install
+      git clone https://huggingface.co/mistralai/Mistral-7B-v0.1
+      ```
+
+   2. Navigate into the new directory and run `docker run --rm -v .:/repo ollama/quantize -q q4_0 /repo`
+   3. Now you can create a modelfile using the q4_0.bin file that was created.
+
 ## Convert and Quantize Manually
 
 ### Clone llama.cpp to your machine
@@ -48,6 +62,7 @@ If we know the model has a chance of working, then we need to convert and quanti
  [`git clone https://github.com/ggerganov/llama.cpp.git`](https://github.com/ggerganov/llama.cpp.git)
     1. If you don't have git installed, download this zip file and unzip it to that location: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.zip
 3. Install the Python dependencies: `pip install torch transformers sentencepiece`
+4. Run 'make' to build the project and the quantize executable.
 
 ### Convert the model to GGUF