Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama2:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
### Durations
@ -66,7 +66,7 @@ Enable JSON mode by setting the `format` parameter to `json`. This will structur
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"model": "llama3",
"prompt": "Why is the sky blue?"
}'
```
@ -77,7 +77,7 @@ A stream of JSON objects is returned:
Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
@ -15,7 +15,7 @@ import { Ollama } from "langchain/llms/ollama";
const ollama = new Ollama({
baseUrl: "http://localhost:11434",
model: "llama2",
model: "llama3",
});
const answer = await ollama.invoke(`why is the sky blue?`);
@ -23,10 +23,10 @@ const answer = await ollama.invoke(`why is the sky blue?`);
console.log(answer);
```
That will get us the same thing as if we ran `ollama run llama2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
That will get us the same thing as if we ran `ollama run llama3 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
prompt=f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
@ -15,7 +15,7 @@ async function characterGenerator() {
ollama.setModel("stablebeluga2:70b-q4_K_M");
constbio=awaitollama.generate(`create a bio of ${character} in a single long paragraph. Instead of saying '${character} is...' or '${character} was...' use language like 'You are...' or 'You were...'. Then create a paragraph describing the speaking mannerisms and style of ${character}. Don't include anything about how ${character} looked or what they sounded like, just focus on the words they said. Instead of saying '${character} would say...' use language like 'You should say...'. If you use quotes, always use single quotes instead of double quotes. If there are any specific words or phrases you used a lot, show how you used them. `);
constthecontents=`FROM llama2\nSYSTEM """\n${bio.response.replace(/(\r\n|\n|\r)/gm," ").replace('would','should')} All answers to questions should be related back to what you are most known for.\n"""`;
constthecontents=`FROM llama3\nSYSTEM """\n${bio.response.replace(/(\r\n|\n|\r)/gm," ").replace('would','should')} All answers to questions should be related back to what you are most known for.\n"""`;
Dr Nic Williams
2 years ago
GitHub