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Update domain name references in docs and install script (#2435)
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@@ -8,7 +8,7 @@
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"outputs": [],
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"source": [
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"# Download and run the Ollama Linux install script\n",
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"!curl https://ollama.ai/install.sh | sh\n",
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"!curl -fsSL https://ollama.com/install.sh | sh\n",
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"!command -v systemctl >/dev/null && sudo systemctl stop ollama"
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]
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},
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@@ -2,28 +2,28 @@
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## Prerequisites
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- Ollama: https://ollama.ai/download
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- Ollama: https://ollama.com/download
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- Kubernetes cluster. This example will use Google Kubernetes Engine.
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## Steps
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1. Create the Ollama namespace, daemon set, and service
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```bash
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kubectl apply -f cpu.yaml
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```
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```bash
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kubectl apply -f cpu.yaml
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```
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1. Port forward the Ollama service to connect and use it locally
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```bash
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kubectl -n ollama port-forward service/ollama 11434:80
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```
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```bash
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kubectl -n ollama port-forward service/ollama 11434:80
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```
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1. Pull and run a model, for example `orca-mini:3b`
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```bash
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ollama run orca-mini:3b
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```
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```bash
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ollama run orca-mini:3b
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```
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## (Optional) Hardware Acceleration
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@@ -1,6 +1,6 @@
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# LangChain Web Summarization
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This example summarizes the website, [https://ollama.ai/blog/run-llama2-uncensored-locally](https://ollama.ai/blog/run-llama2-uncensored-locally)
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This example summarizes the website, [https://ollama.com/blog/run-llama2-uncensored-locally](https://ollama.com/blog/run-llama2-uncensored-locally)
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## Running the Example
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@@ -2,7 +2,7 @@ from langchain.llms import Ollama
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from langchain.document_loaders import WebBaseLoader
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from langchain.chains.summarize import load_summarize_chain
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loader = WebBaseLoader("https://ollama.ai/blog/run-llama2-uncensored-locally")
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loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
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docs = loader.load()
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llm = Ollama(model="llama2")
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@@ -40,13 +40,13 @@ You are a log file analyzer. You will receive a set of lines from a log file for
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"""
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```
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This model is available at https://ollama.ai/mattw/loganalyzer. You can customize it and add to your own namespace using the command `ollama create <namespace/modelname> -f <path-to-modelfile>` then `ollama push <namespace/modelname>`.
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This model is available at https://ollama.com/mattw/loganalyzer. You can customize it and add to your own namespace using the command `ollama create <namespace/modelname> -f <path-to-modelfile>` then `ollama push <namespace/modelname>`.
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Then loganalysis.py scans all the lines in the given log file and searches for the word 'error'. When the word is found, the 10 lines before and after are set as the prompt for a call to the Generate API.
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```python
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data = {
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"prompt": "\n".join(error_logs),
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"prompt": "\n".join(error_logs),
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"model": "mattw/loganalyzer"
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}
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```
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@@ -29,9 +29,9 @@ You can also add your own character to be chosen at random when you ask a questi
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```bash
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ollama pull stablebeluga2:70b-q4_K_M
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```
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2. Create a new character:
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```bash
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npm run charactergen "Lorne Greene"
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```
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@@ -41,15 +41,15 @@ You can also add your own character to be chosen at random when you ask a questi
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3. Now you can create a model with this command:
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```bash
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ollama create <YourNamespace>/lornegreene -f lornegreene/Modelfile
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ollama create <username>/lornegreene -f lornegreene/Modelfile
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```
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`YourNamespace` is whatever name you set up when you signed up at [https://ollama.ai/signup](https://ollama.ai/signup).
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`username` is whatever name you set up when you signed up at [https://ollama.com/signup](https://ollama.com/signup).
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4. To add this to your mentors, you will have to update the code as follows. On line 8 of `mentors.ts`, add an object to the array, replacing `<YourNamespace>` with the namespace you used above.
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4. To add this to your mentors, you will have to update the code as follows. On line 8 of `mentors.ts`, add an object to the array, replacing `<username>` with the username you used above.
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```bash
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{ns: "<YourNamespace>", char: "Lorne Greene"}
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{ns: "<username>", char: "Lorne Greene"}
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```
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## Review the Code
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