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Update 'llama2' -> 'llama3' in most places (#4116)

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* Update 'llama2' -> 'llama3' in most places

---------

Co-authored-by: Patrick Devine <patrick@infrahq.com>
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Dr Nic Williams 2024-05-04 05:25:04 +10:00 committed by GitHub
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58
docs/api.md
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@ -17,7 +17,7 @@
### Model names ### Model names
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 ### Durations
@ -66,7 +66,7 @@ Enable JSON mode by setting the `format` parameter to `json`. This will structur
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2", "model": "llama3",
"prompt": "Why is the sky blue?" "prompt": "Why is the sky blue?"
}' }'
``` ```
@ -77,7 +77,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"response": "The", "response": "The",
"done": false "done": false
@ -99,7 +99,7 @@ To calculate how fast the response is generated in tokens per second (token/s),
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "", "response": "",
"done": true, "done": true,
@ -121,7 +121,7 @@ A response can be received in one reply when streaming is off.
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2", "model": "llama3",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false "stream": false
}' }'
@ -133,7 +133,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "The sky is blue because it is the color of the sky.", "response": "The sky is blue because it is the color of the sky.",
"done": true, "done": true,
@ -155,7 +155,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2", "model": "llama3",
"prompt": "What color is the sky at different times of the day? Respond using JSON", "prompt": "What color is the sky at different times of the day? Respond using JSON",
"format": "json", "format": "json",
"stream": false "stream": false
@ -166,7 +166,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-11-09T21:07:55.186497Z", "created_at": "2023-11-09T21:07:55.186497Z",
"response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n", "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
"done": true, "done": true,
@ -289,7 +289,7 @@ If you want to set custom options for the model at runtime rather than in the Mo
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2", "model": "llama3",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false, "stream": false,
"options": { "options": {
@ -332,7 +332,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "The sky is blue because it is the color of the sky.", "response": "The sky is blue because it is the color of the sky.",
"done": true, "done": true,
@ -354,7 +354,7 @@ If an empty prompt is provided, the model will be loaded into memory.
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2" "model": "llama3"
}' }'
``` ```
@ -364,7 +364,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-12-18T19:52:07.071755Z", "created_at": "2023-12-18T19:52:07.071755Z",
"response": "", "response": "",
"done": true "done": true
@ -407,7 +407,7 @@ Send a chat message with a streaming response.
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama2", "model": "llama3",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -423,7 +423,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -438,7 +438,7 @@ Final response:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"done": true, "done": true,
"total_duration": 4883583458, "total_duration": 4883583458,
@ -456,7 +456,7 @@ Final response:
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama2", "model": "llama3",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -471,7 +471,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "registry.ollama.ai/library/llama2:latest", "model": "registry.ollama.ai/library/llama3:latest",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -495,7 +495,7 @@ Send a chat message with a conversation history. You can use this same approach
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama2", "model": "llama3",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -519,7 +519,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -533,7 +533,7 @@ Final response:
```json ```json
{ {
"model": "llama2", "model": "llama3",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"done": true, "done": true,
"total_duration": 8113331500, "total_duration": 8113331500,
@ -591,7 +591,7 @@ curl http://localhost:11434/api/chat -d '{
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama2", "model": "llama3",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -609,7 +609,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "registry.ollama.ai/library/llama2:latest", "model": "registry.ollama.ai/library/llama3:latest",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -651,7 +651,7 @@ Create a new model from a `Modelfile`.
```shell ```shell
curl http://localhost:11434/api/create -d '{ curl http://localhost:11434/api/create -d '{
"name": "mario", "name": "mario",
"modelfile": "FROM llama2\nSYSTEM You are mario from Super Mario Bros." "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
}' }'
``` ```
@ -758,7 +758,7 @@ A single JSON object will be returned.
} }
}, },
{ {
"name": "llama2:latest", "name": "llama3:latest",
"modified_at": "2023-12-07T09:32:18.757212583-08:00", "modified_at": "2023-12-07T09:32:18.757212583-08:00",
"size": 3825819519, "size": 3825819519,
"digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e", "digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
@ -792,7 +792,7 @@ Show information about a model including details, modelfile, template, parameter
```shell ```shell
curl http://localhost:11434/api/show -d '{ curl http://localhost:11434/api/show -d '{
"name": "llama2" "name": "llama3"
}' }'
``` ```
@ -827,8 +827,8 @@ Copy a model. Creates a model with another name from an existing model.
```shell ```shell
curl http://localhost:11434/api/copy -d '{ curl http://localhost:11434/api/copy -d '{
"source": "llama2", "source": "llama3",
"destination": "llama2-backup" "destination": "llama3-backup"
}' }'
``` ```
@ -854,7 +854,7 @@ Delete a model and its data.
```shell ```shell
curl -X DELETE http://localhost:11434/api/delete -d '{ curl -X DELETE http://localhost:11434/api/delete -d '{
"name": "llama2:13b" "name": "llama3:13b"
}' }'
``` ```
@ -882,7 +882,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
```shell ```shell
curl http://localhost:11434/api/pull -d '{ curl http://localhost:11434/api/pull -d '{
"name": "llama2" "name": "llama3"
}' }'
``` ```

10
docs/faq.md
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@ -32,7 +32,7 @@ When using the API, specify the `num_ctx` parameter:
``` ```
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama2", "model": "llama3",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"options": { "options": {
"num_ctx": 4096 "num_ctx": 4096
@ -88,9 +88,9 @@ On windows, Ollama inherits your user and system environment variables.
3. Edit or create New variable(s) for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc. 3. Edit or create New variable(s) for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
4. Click OK/Apply to save 4. Click OK/Apply to save
5. Run `ollama` from a new terminal window 5. Run `ollama` from a new terminal window
## How can I expose Ollama on my network? ## How can I expose Ollama on my network?
@ -221,12 +221,12 @@ The `keep_alive` parameter can be set to:
For example, to preload a model and leave it in memory use: For example, to preload a model and leave it in memory use:
```shell ```shell
curl http://localhost:11434/api/generate -d '{"model": "llama2", "keep_alive": -1}' curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": -1}'
``` ```
To unload the model and free up memory use: To unload the model and free up memory use:
```shell ```shell
curl http://localhost:11434/api/generate -d '{"model": "llama2", "keep_alive": 0}' curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": 0}'
``` ```
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. 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.

42
docs/modelfile.md
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@ -10,7 +10,7 @@ A model file is the blueprint to create and share models with Ollama.
- [Examples](#examples) - [Examples](#examples)
- [Instructions](#instructions) - [Instructions](#instructions)
- [FROM (Required)](#from-required) - [FROM (Required)](#from-required)
- [Build from llama2](#build-from-llama2) - [Build from llama3](#build-from-llama3)
- [Build from a bin file](#build-from-a-bin-file) - [Build from a bin file](#build-from-a-bin-file)
- [PARAMETER](#parameter) - [PARAMETER](#parameter)
- [Valid Parameters and Values](#valid-parameters-and-values) - [Valid Parameters and Values](#valid-parameters-and-values)
@ -48,7 +48,7 @@ INSTRUCTION arguments
An example of a `Modelfile` creating a mario blueprint: An example of a `Modelfile` creating a mario blueprint:
```modelfile ```modelfile
FROM llama2 FROM llama3
# sets the temperature to 1 [higher is more creative, lower is more coherent] # sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1 PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token # sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
@ -67,33 +67,25 @@ To use this:
More examples are available in the [examples directory](../examples). More examples are available in the [examples directory](../examples).
### `Modelfile`s in [ollama.com/library][1] To view the Modelfile of a given model, use the `ollama show --modelfile` command.
There are two ways to view `Modelfile`s underlying the models in [ollama.com/library][1]:
- Option 1: view a details page from a model's tags page:
1. Go to a particular model's tags (e.g. https://ollama.com/library/llama2/tags)
2. Click on a tag (e.g. https://ollama.com/library/llama2:13b)
3. Scroll down to "Layers"
- Note: if the [`FROM` instruction](#from-required) is not present,
it means the model was created from a local file
- Option 2: use `ollama show` to print the `Modelfile` for any local models like so:
```bash ```bash
> ollama show --modelfile llama2:13b > ollama show --modelfile llama3
# Modelfile generated by "ollama show" # Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with: # To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama2:13b # FROM llama3:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
FROM /root/.ollama/models/blobs/sha256:123abc {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
TEMPLATE """[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>>
{{ end }}{{ .Prompt }} [/INST] """ {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
SYSTEM """"""
PARAMETER stop [INST] {{ .Response }}<|eot_id|>"""
PARAMETER stop [/INST] PARAMETER stop "<|start_header_id|>"
PARAMETER stop <<SYS>> PARAMETER stop "<|end_header_id|>"
PARAMETER stop <</SYS>> PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"
``` ```
## Instructions ## Instructions
@ -106,10 +98,10 @@ The `FROM` instruction defines the base model to use when creating a model.
FROM <model name>:<tag> FROM <model name>:<tag>
``` ```
#### Build from llama2 #### Build from llama3
```modelfile ```modelfile
FROM llama2 FROM llama3
``` ```
A list of available base models: A list of available base models:

10
docs/openai.md
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@ -25,7 +25,7 @@ chat_completion = client.chat.completions.create(
'content': 'Say this is a test', 'content': 'Say this is a test',
} }
], ],
model='llama2', model='llama3',
) )
``` ```
@ -43,7 +43,7 @@ const openai = new OpenAI({
const chatCompletion = await openai.chat.completions.create({ const chatCompletion = await openai.chat.completions.create({
messages: [{ role: 'user', content: 'Say this is a test' }], messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'llama2', model: 'llama3',
}) })
``` ```
@ -53,7 +53,7 @@ const chatCompletion = await openai.chat.completions.create({
curl http://localhost:11434/v1/chat/completions \ curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "llama2", "model": "llama3",
"messages": [ "messages": [
{ {
"role": "system", "role": "system",
@ -113,7 +113,7 @@ curl http://localhost:11434/v1/chat/completions \
Before using a model, pull it locally `ollama pull`: Before using a model, pull it locally `ollama pull`:
```shell ```shell
ollama pull llama2 ollama pull llama3
``` ```
### Default model names ### Default model names
@ -121,7 +121,7 @@ ollama pull llama2
For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name: For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
``` ```
ollama cp llama2 gpt-3.5-turbo ollama cp llama3 gpt-3.5-turbo
``` ```
Afterwards, this new model name can be specified the `model` field: Afterwards, this new model name can be specified the `model` field:

6
docs/tutorials/langchainjs.md
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@ -15,7 +15,7 @@ import { Ollama } from "langchain/llms/ollama";
const ollama = new Ollama({ const ollama = new Ollama({
baseUrl: "http://localhost:11434", baseUrl: "http://localhost:11434",
model: "llama2", model: "llama3",
}); });
const answer = await ollama.invoke(`why is the sky blue?`); 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); 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.
```bash ```bash
npm install cheerio npm install cheerio
``` ```
```javascript ```javascript

3
docs/windows.md
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@ -1,3 +1,4 @@
<<<<<<< HEAD
# Ollama Windows Preview # Ollama Windows Preview
Welcome to the Ollama Windows preview. Welcome to the Ollama Windows preview.
@ -27,7 +28,7 @@ Logs will often be helpful in diagnosing the problem (see
Here's a quick example showing API access from `powershell` Here's a quick example showing API access from `powershell`
```powershell ```powershell
(Invoke-WebRequest -method POST -Body '{"model":"llama2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json (Invoke-WebRequest -method POST -Body '{"model":"llama3", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
``` ```
## Troubleshooting ## Troubleshooting

2
examples/bash-comparemodels/README.md
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@ -2,7 +2,7 @@
When calling `ollama`, you can pass it a file to run all the prompts in the file, one after the other: When calling `ollama`, you can pass it a file to run all the prompts in the file, one after the other:
`ollama run llama2 < sourcequestions.txt` `ollama run llama3 < sourcequestions.txt`
This concept is used in the following example. This concept is used in the following example.

2
examples/go-chat/main.go
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@ -35,7 +35,7 @@ func main() {
ctx := context.Background() ctx := context.Background()
req := &api.ChatRequest{ req := &api.ChatRequest{
Model: "llama2", Model: "llama3",
Messages: messages, Messages: messages,
} }

10
examples/langchain-python-rag-document/main.py
View file

@ -40,9 +40,9 @@ while True:
continue continue
# Prompt # Prompt
template = """Use the following pieces of context to answer the question at the end. template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible. Use three sentences maximum and keep the answer as concise as possible.
{context} {context}
Question: {question} Question: {question}
Helpful Answer:""" Helpful Answer:"""
@ -51,11 +51,11 @@ while True:
template=template, template=template,
) )
llm = Ollama(model="llama2:13b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])) llm = Ollama(model="llama3:8b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
qa_chain = RetrievalQA.from_chain_type( qa_chain = RetrievalQA.from_chain_type(
llm, llm,
retriever=vectorstore.as_retriever(), retriever=vectorstore.as_retriever(),
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}, chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
) )
result = qa_chain({"query": query}) result = qa_chain({"query": query})

5
examples/langchain-python-simple/README.md
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@ -4,10 +4,10 @@ This example is a basic "hello world" of using LangChain with Ollama.
## Running the Example ## Running the Example
1. Ensure you have the `llama2` model installed: 1. Ensure you have the `llama3` model installed:
```bash ```bash
ollama pull llama2 ollama pull llama3
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.
@ -21,4 +21,3 @@ This example is a basic "hello world" of using LangChain with Ollama.
```bash ```bash
python main.py python main.py
``` ```

2
examples/langchain-python-simple/main.py
View file

@ -1,6 +1,6 @@
from langchain.llms import Ollama from langchain.llms import Ollama
input = input("What is your question?") input = input("What is your question?")
llm = Ollama(model="llama2") llm = Ollama(model="llama3")
res = llm.predict(input) res = llm.predict(input)
print (res) print (res)

2
examples/modelfile-mario/Modelfile
View file

@ -1,4 +1,4 @@
FROM llama2 FROM llama3
PARAMETER temperature 1 PARAMETER temperature 1
SYSTEM """ SYSTEM """
You are Mario from super mario bros, acting as an assistant. You are Mario from super mario bros, acting as an assistant.

6
examples/modelfile-mario/readme.md
View file

@ -2,12 +2,12 @@
# Example character: Mario # Example character: Mario
This example shows how to create a basic character using Llama2 as the base model. This example shows how to create a basic character using Llama3 as the base model.
To run this example: To run this example:
1. Download the Modelfile 1. Download the Modelfile
2. `ollama pull llama2` to get the base model used in the model file. 2. `ollama pull llama3` to get the base model used in the model file.
3. `ollama create NAME -f ./Modelfile` 3. `ollama create NAME -f ./Modelfile`
4. `ollama run NAME` 4. `ollama run NAME`
@ -18,7 +18,7 @@ Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
What the model file looks like: What the model file looks like:
``` ```
FROM llama2 FROM llama3
PARAMETER temperature 1 PARAMETER temperature 1
SYSTEM """ SYSTEM """
You are Mario from Super Mario Bros, acting as an assistant. You are Mario from Super Mario Bros, acting as an assistant.

14
examples/python-json-datagenerator/predefinedschema.py
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@ -2,16 +2,16 @@ import requests
import json import json
import random import random
model = "llama2" model = "llama3"
template = { template = {
"firstName": "", "firstName": "",
"lastName": "", "lastName": "",
"address": { "address": {
"street": "", "street": "",
"city": "", "city": "",
"state": "", "state": "",
"zipCode": "" "zipCode": ""
}, },
"phoneNumber": "" "phoneNumber": ""
} }

2
examples/python-json-datagenerator/randomaddresses.py
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@ -12,7 +12,7 @@ countries = [
"France", "France",
] ]
country = random.choice(countries) country = random.choice(countries)
model = "llama2" model = "llama3"
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." 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."

4
examples/python-json-datagenerator/readme.md
View file

@ -6,10 +6,10 @@ There are two python scripts in this example. `randomaddresses.py` generates ran
## Running the Example ## Running the Example
1. Ensure you have the `llama2` model installed: 1. Ensure you have the `llama3` model installed:
```bash ```bash
ollama pull llama2 ollama pull llama3
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/python-simplechat/client.py
View file

@ -2,7 +2,7 @@ import json
import requests import requests
# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve` # NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
model = "llama2" # TODO: update this for whatever model you wish to use model = "llama3" # TODO: update this for whatever model you wish to use
def chat(messages): def chat(messages):

4
examples/python-simplechat/readme.md
View file

@ -4,10 +4,10 @@ The **chat** endpoint is one of two ways to generate text from an LLM with Ollam
## Running the Example ## Running the Example
1. Ensure you have the `llama2` model installed: 1. Ensure you have the `llama3` model installed:
```bash ```bash
ollama pull llama2 ollama pull llama3
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

4
examples/typescript-mentors/README.md
View file

@ -4,10 +4,10 @@ This example demonstrates how one would create a set of 'mentors' you can have a
## Usage ## Usage
1. Add llama2 to have the mentors ask your questions: 1. Add llama3 to have the mentors ask your questions:
```bash ```bash
ollama pull llama2 ollama pull llama3
``` ```
2. Install prerequisites: 2. Install prerequisites:

4
examples/typescript-mentors/character-generator.ts
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@ -15,7 +15,7 @@ async function characterGenerator() {
ollama.setModel("stablebeluga2:70b-q4_K_M"); ollama.setModel("stablebeluga2:70b-q4_K_M");
const bio = await ollama.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. `); const bio = await ollama.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. `);
const thecontents = `FROM llama2\nSYSTEM """\n${bio.response.replace(/(\r\n|\n|\r)/gm, " ").replace('would', 'should')} All answers to questions should be related back to what you are most known for.\n"""`; const thecontents = `FROM llama3\nSYSTEM """\n${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"""`;
fs.writeFile(path.join(directory, 'Modelfile'), thecontents, (err: any) => { fs.writeFile(path.join(directory, 'Modelfile'), thecontents, (err: any) => {
if (err) throw err; if (err) throw err;
@ -23,4 +23,4 @@ async function characterGenerator() {
}); });
} }
characterGenerator(); characterGenerator();

4
examples/typescript-simplechat/client.ts
View file

@ -1,6 +1,6 @@
import * as readline from "readline"; import * as readline from "readline";
const model = "llama2"; const model = "llama3";
type Message = { type Message = {
role: "assistant" | "user" | "system"; role: "assistant" | "user" | "system";
content: string; content: string;
@ -74,4 +74,4 @@ async function main() {
} }
main(); main();