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2
.github/workflows/release.yaml vendored
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@ -354,7 +354,7 @@ jobs:
- name: Set Version - name: Set Version
run: | run: |
$ver=${env:GITHUB_REF_NAME}.trim("v") $ver=${env:GITHUB_REF_NAME}.trim("v")
write-host VERSION=$ver | Out-File -FilePath ${env:GITHUB_ENV} -Encoding utf8 -Append echo VERSION=$ver | Out-File -FilePath ${env:GITHUB_ENV} -Encoding utf8 -Append
- uses: 'google-github-actions/auth@v2' - uses: 'google-github-actions/auth@v2'
with: with:
project_id: 'ollama' project_id: 'ollama'

33
README.md
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@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
## Quickstart ## Quickstart
To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1): To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
``` ```
ollama run llama3.1 ollama run llama3.2
``` ```
## Model library ## Model library
@ -49,6 +49,8 @@ Here are some example models that can be downloaded:
| Model | Parameters | Size | Download | | Model | Parameters | Size | Download |
| ------------------ | ---------- | ----- | ------------------------------ | | ------------------ | ---------- | ----- | ------------------------------ |
| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` | | Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` | | Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` |
| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` | | Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
@ -99,16 +101,16 @@ See the [guide](docs/import.md) on importing models for more information.
### Customize a prompt ### Customize a prompt
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model: Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
Create a `Modelfile`: Create a `Modelfile`:
``` ```
FROM llama3.1 FROM llama3.2
# set the temperature to 1 [higher is more creative, lower is more coherent] # set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1 PARAMETER temperature 1
@ -143,7 +145,7 @@ ollama create mymodel -f ./Modelfile
### Pull a model ### Pull a model
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
> This command can also be used to update a local model. Only the diff will be pulled. > This command can also be used to update a local model. Only the diff will be pulled.
@ -151,13 +153,13 @@ ollama pull llama3.1
### Remove a model ### Remove a model
``` ```
ollama rm llama3.1 ollama rm llama3.2
``` ```
### Copy a model ### Copy a model
``` ```
ollama cp llama3.1 my-model ollama cp llama3.2 my-model
``` ```
### Multiline input ### Multiline input
@ -181,14 +183,14 @@ The image features a yellow smiley face, which is likely the central focus of th
### Pass the prompt as an argument ### Pass the prompt as an argument
``` ```
$ ollama run llama3.1 "Summarize this file: $(cat README.md)" $ ollama run llama3.2 "Summarize this file: $(cat README.md)"
Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
``` ```
### Show model information ### Show model information
``` ```
ollama show llama3.1 ollama show llama3.2
``` ```
### List models on your computer ### List models on your computer
@ -206,7 +208,7 @@ ollama ps
### Stop a model which is currently running ### Stop a model which is currently running
``` ```
ollama stop llama3.1 ollama stop llama3.2
``` ```
### Start Ollama ### Start Ollama
@ -228,7 +230,7 @@ Next, start the server:
Finally, in a separate shell, run a model: Finally, in a separate shell, run a model:
``` ```
./ollama run llama3.1 ./ollama run llama3.2
``` ```
## REST API ## REST API
@ -239,7 +241,7 @@ Ollama has a REST API for running and managing models.
``` ```
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt":"Why is the sky blue?" "prompt":"Why is the sky blue?"
}' }'
``` ```
@ -248,7 +250,7 @@ curl http://localhost:11434/api/generate -d '{
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ "role": "user", "content": "why is the sky blue?" } { "role": "user", "content": "why is the sky blue?" }
] ]
@ -325,6 +327,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption) - [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library) - [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
- [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama) - [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
### Terminal ### Terminal
@ -377,7 +380,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example) - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java) - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs) - [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html) - [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
- [LiteLLM](https://github.com/BerriAI/litellm) - [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm) - [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp) - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)

2
app/ollama.iss
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@ -142,7 +142,7 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
;FinishedHeadingLabel=Run your first model ;FinishedHeadingLabel=Run your first model
;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama3.1 ;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama3.2
;ClickFinish=%n ;ClickFinish=%n
[Registry] [Registry]

2
app/ollama_welcome.ps1
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@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
write-host "" write-host ""
write-host "Run your first model:" write-host "Run your first model:"
write-host "" write-host ""
write-host "`tollama run llama3.1" write-host "`tollama run llama3.2"
write-host "" write-host ""

64
docs/api.md
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@ -69,7 +69,7 @@ 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": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?" "prompt": "Why is the sky blue?"
}' }'
``` ```
@ -80,7 +80,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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
@ -102,7 +102,7 @@ To calculate how fast the response is generated in tokens per second (token/s),
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "", "response": "",
"done": true, "done": true,
@ -124,7 +124,7 @@ 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": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false "stream": false
}' }'
@ -136,7 +136,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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,
@ -194,7 +194,7 @@ curl http://localhost:11434/api/generate -d '{
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"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
@ -205,7 +205,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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,
@ -327,7 +327,7 @@ 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": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false, "stream": false,
"options": { "options": {
@ -368,7 +368,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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,
@ -390,7 +390,7 @@ 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": "llama3.1" "model": "llama3.2"
}' }'
``` ```
@ -400,7 +400,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-18T19:52:07.071755Z", "created_at": "2023-12-18T19:52:07.071755Z",
"response": "", "response": "",
"done": true "done": true
@ -415,7 +415,7 @@ If an empty prompt is provided and the `keep_alive` parameter is set to `0`, a m
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"keep_alive": 0 "keep_alive": 0
}' }'
``` ```
@ -426,7 +426,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2024-09-12T03:54:03.516566Z", "created_at": "2024-09-12T03:54:03.516566Z",
"response": "", "response": "",
"done": true, "done": true,
@ -472,7 +472,7 @@ 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": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -488,7 +488,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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",
@ -503,7 +503,7 @@ Final response:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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,
@ -521,7 +521,7 @@ Final response:
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -536,7 +536,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -560,7 +560,7 @@ 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": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -584,7 +584,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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",
@ -598,7 +598,7 @@ Final response:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"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,
@ -656,7 +656,7 @@ curl http://localhost:11434/api/chat -d '{
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -674,7 +674,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -696,7 +696,7 @@ curl http://localhost:11434/api/chat -d '{
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@ -735,7 +735,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2024-07-22T20:33:28.123648Z", "created_at": "2024-07-22T20:33:28.123648Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -771,7 +771,7 @@ If the messages array is empty, the model will be loaded into memory.
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [] "messages": []
}' }'
``` ```
@ -779,7 +779,7 @@ curl http://localhost:11434/api/chat -d '{
##### Response ##### Response
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at":"2024-09-12T21:17:29.110811Z", "created_at":"2024-09-12T21:17:29.110811Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -798,7 +798,7 @@ If the messages array is empty and the `keep_alive` parameter is set to `0`, a m
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [], "messages": [],
"keep_alive": 0 "keep_alive": 0
}' }'
@ -810,7 +810,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at":"2024-09-12T21:33:17.547535Z", "created_at":"2024-09-12T21:33:17.547535Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@ -989,7 +989,7 @@ 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": "llama3.1" "name": "llama3.2"
}' }'
``` ```
@ -1050,7 +1050,7 @@ 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": "llama3.1", "source": "llama3.2",
"destination": "llama3-backup" "destination": "llama3-backup"
}' }'
``` ```
@ -1105,7 +1105,7 @@ 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": "llama3.1" "name": "llama3.2"
}' }'
``` ```

2
docs/docker.md
View file

@ -63,7 +63,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
Now you can run a model: Now you can run a model:
``` ```
docker exec -it ollama ollama run llama3.1 docker exec -it ollama ollama run llama3.2
``` ```
### Try different models ### Try different models

10
docs/faq.md
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@ -32,7 +32,7 @@ When using the API, specify the `num_ctx` parameter:
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"options": { "options": {
"num_ctx": 4096 "num_ctx": 4096
@ -232,7 +232,7 @@ curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
To preload a model using the CLI, use the command: To preload a model using the CLI, use the command:
```shell ```shell
ollama run llama3.1 "" ollama run llama3.2 ""
``` ```
## How do I keep a model loaded in memory or make it unload immediately? ## How do I keep a model loaded in memory or make it unload immediately?
@ -240,7 +240,7 @@ ollama run llama3.1 ""
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command: By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command:
```shell ```shell
ollama stop llama3.1 ollama stop llama3.2
``` ```
If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to: If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to:
@ -251,12 +251,12 @@ If you're using the API, use the `keep_alive` parameter with the `/api/generate`
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": "llama3.1", "keep_alive": -1}' curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "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": "llama3.1", "keep_alive": 0}' curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "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 the 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 the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.

8
docs/modelfile.md
View file

@ -50,7 +50,7 @@ INSTRUCTION arguments
An example of a `Modelfile` creating a mario blueprint: An example of a `Modelfile` creating a mario blueprint:
```modelfile ```modelfile
FROM llama3.1 FROM llama3.2
# 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
@ -72,10 +72,10 @@ More examples are available in the [examples directory](../examples).
To view the Modelfile of a given model, use the `ollama show --modelfile` command. To view the Modelfile of a given model, use the `ollama show --modelfile` command.
```bash ```bash
> ollama show --modelfile llama3.1 > ollama show --modelfile llama3.2
# 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 llama3.1:latest # FROM llama3.2:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29 FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|> TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
@ -103,7 +103,7 @@ FROM <model name>:<tag>
#### Build from existing model #### Build from existing model
```modelfile ```modelfile
FROM llama3.1 FROM llama3.2
``` ```
A list of available base models: A list of available base models:

22
docs/openai.md
View file

@ -25,7 +25,7 @@ chat_completion = client.chat.completions.create(
'content': 'Say this is a test', 'content': 'Say this is a test',
} }
], ],
model='llama3.1', model='llama3.2',
) )
response = client.chat.completions.create( response = client.chat.completions.create(
@ -46,13 +46,13 @@ response = client.chat.completions.create(
) )
completion = client.completions.create( completion = client.completions.create(
model="llama3.1", model="llama3.2",
prompt="Say this is a test", prompt="Say this is a test",
) )
list_completion = client.models.list() list_completion = client.models.list()
model = client.models.retrieve("llama3.1") model = client.models.retrieve("llama3.2")
embeddings = client.embeddings.create( embeddings = client.embeddings.create(
model="all-minilm", model="all-minilm",
@ -74,7 +74,7 @@ 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: 'llama3.1', model: 'llama3.2',
}) })
const response = await openai.chat.completions.create({ const response = await openai.chat.completions.create({
@ -94,13 +94,13 @@ const response = await openai.chat.completions.create({
}) })
const completion = await openai.completions.create({ const completion = await openai.completions.create({
model: "llama3.1", model: "llama3.2",
prompt: "Say this is a test.", prompt: "Say this is a test.",
}) })
const listCompletion = await openai.models.list() const listCompletion = await openai.models.list()
const model = await openai.models.retrieve("llama3.1") const model = await openai.models.retrieve("llama3.2")
const embedding = await openai.embeddings.create({ const embedding = await openai.embeddings.create({
model: "all-minilm", model: "all-minilm",
@ -114,7 +114,7 @@ const embedding = await openai.embeddings.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": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "system", "role": "system",
@ -154,13 +154,13 @@ curl http://localhost:11434/v1/chat/completions \
curl http://localhost:11434/v1/completions \ curl http://localhost:11434/v1/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Say this is a test" "prompt": "Say this is a test"
}' }'
curl http://localhost:11434/v1/models curl http://localhost:11434/v1/models
curl http://localhost:11434/v1/models/llama3.1 curl http://localhost:11434/v1/models/llama3.2
curl http://localhost:11434/v1/embeddings \ curl http://localhost:11434/v1/embeddings \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
@ -274,7 +274,7 @@ curl http://localhost:11434/v1/embeddings \
Before using a model, pull it locally `ollama pull`: Before using a model, pull it locally `ollama pull`:
```shell ```shell
ollama pull llama3.1 ollama pull llama3.2
``` ```
### Default model names ### Default model names
@ -282,7 +282,7 @@ ollama pull llama3.1
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 llama3.1 gpt-3.5-turbo ollama cp llama3.2 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:

2
docs/template.md
View file

@ -33,7 +33,7 @@ Omitting a template in these models puts the responsibility of correctly templat
To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3. To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
```dockerfile ```dockerfile
FROM llama3.1 FROM llama3.2
TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|> TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>

4
docs/tutorials/langchainjs.md
View file

@ -15,7 +15,7 @@ import { Ollama } from "@langchain/community/llms/ollama";
const ollama = new Ollama({ const ollama = new Ollama({
baseUrl: "http://localhost:11434", baseUrl: "http://localhost:11434",
model: "llama3.1", model: "llama3.2",
}); });
const answer = await ollama.invoke(`why is the sky blue?`); const answer = await ollama.invoke(`why is the sky blue?`);
@ -23,7 +23,7 @@ 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 llama3.1 "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 llama3.2 "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

2
docs/windows.md
View file

@ -29,7 +29,7 @@ Ollama uses unicode characters for progress indication, which may render as unkn
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":"llama3.1", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json (Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
``` ```
## Troubleshooting ## Troubleshooting

2
examples/go-chat/main.go
View file

@ -35,7 +35,7 @@ func main() {
ctx := context.Background() ctx := context.Background()
req := &api.ChatRequest{ req := &api.ChatRequest{
Model: "llama3.1", Model: "llama3.2",
Messages: messages, Messages: messages,
} }

4
examples/langchain-python-rag-document/README.md
View file

@ -4,10 +4,10 @@ This example provides an interface for asking questions to a PDF document.
## Setup ## Setup
1. Ensure you have the `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/langchain-python-rag-document/main.py
View file

@ -51,7 +51,7 @@ while True:
template=template, template=template,
) )
llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])) llm = Ollama(model="llama3.2", 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(),

4
examples/langchain-python-rag-websummary/README.md
View file

@ -4,10 +4,10 @@ This example summarizes the website, [https://ollama.com/blog/run-llama2-uncenso
## Running the Example ## Running the Example
1. Ensure you have the `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
```bash ```bash
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/langchain-python-rag-websummary/main.py
View file

@ -5,7 +5,7 @@ from langchain.chains.summarize import load_summarize_chain
loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally") loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
docs = loader.load() docs = loader.load()
llm = Ollama(model="llama3.1") llm = Ollama(model="llama3.2")
chain = load_summarize_chain(llm, chain_type="stuff") chain = load_summarize_chain(llm, chain_type="stuff")
result = chain.invoke(docs) result = chain.invoke(docs)

4
examples/langchain-python-simple/README.md
View file

@ -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 `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
```bash ```bash
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

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="llama3.1") llm = Ollama(model="llama3.2")
res = llm.predict(input) res = llm.predict(input)
print (res) print (res)

2
examples/modelfile-mario/Modelfile
View file

@ -1,4 +1,4 @@
FROM llama3.1 FROM llama3.2
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 Llama3.1 as the base model. This example shows how to create a basic character using Llama 3.2 as the base model.
To run this example: To run this example:
1. Download the Modelfile 1. Download the Modelfile
2. `ollama pull llama3.1` to get the base model used in the model file. 2. `ollama pull llama3.2` 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 llama3.1 FROM llama3.2
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/python-grounded-factuality-rag-check/README.md
View file

@ -1,14 +1,14 @@
# RAG Hallucination Checker using Bespoke-Minicheck # RAG Hallucination Checker using Bespoke-Minicheck
This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.1` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations. This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations.
## Running the Example ## Running the Example
1. Ensure `all-minilm` (embedding) `llama3.1` (chat) and `bespoke-minicheck` (check) models installed: 1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
```bash ```bash
ollama pull all-minilm ollama pull all-minilm
ollama pull llama3.1 ollama pull llama3.2
ollama pull bespoke-minicheck ollama pull bespoke-minicheck
``` ```

4
examples/python-grounded-factuality-rag-check/main.py
View file

@ -9,7 +9,7 @@ import nltk
warnings.filterwarnings( warnings.filterwarnings(
"ignore", category=FutureWarning, module="transformers.tokenization_utils_base" "ignore", category=FutureWarning, module="transformers.tokenization_utils_base"
) )
nltk.download("punkt", quiet=True) nltk.download("punkt_tab", quiet=True)
def getArticleText(url): def getArticleText(url):
@ -119,7 +119,7 @@ if __name__ == "__main__":
system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}" system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
ollama_response = ollama.generate( ollama_response = ollama.generate(
model="llama3.1", model="llama3.2",
prompt=question, prompt=question,
system=system_prompt, system=system_prompt,
options={"stream": False}, options={"stream": False},

2
examples/python-json-datagenerator/predefinedschema.py
View file

@ -2,7 +2,7 @@ import requests
import json import json
import random import random
model = "llama3.1" model = "llama3.2"
template = { template = {
"firstName": "", "firstName": "",
"lastName": "", "lastName": "",

2
examples/python-json-datagenerator/randomaddresses.py
View file

@ -12,7 +12,7 @@ countries = [
"France", "France",
] ]
country = random.choice(countries) country = random.choice(countries)
model = "llama3.1" model = "llama3.2"
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 `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
```bash ```bash
ollama pull llama3.1 ollama pull llama3.2
``` ```
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 = "llama3.1" # TODO: update this for whatever model you wish to use model = "llama3.2" # 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 `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
```bash ```bash
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/typescript-simplechat/client.ts
View file

@ -1,6 +1,6 @@
import * as readline from "readline"; import * as readline from "readline";
const model = "llama3.1"; const model = "llama3.2";
type Message = { type Message = {
role: "assistant" | "user" | "system"; role: "assistant" | "user" | "system";
content: string; content: string;

5
gpu/gpu.go
View file

@ -205,6 +205,8 @@ func GetGPUInfo() GpuInfoList {
if err != nil { if err != nil {
slog.Warn("error looking up system memory", "error", err) slog.Warn("error looking up system memory", "error", err)
} }
depPath := LibraryDir()
cpus = []CPUInfo{ cpus = []CPUInfo{
{ {
GpuInfo: GpuInfo{ GpuInfo: GpuInfo{
@ -212,6 +214,7 @@ func GetGPUInfo() GpuInfoList {
Library: "cpu", Library: "cpu",
Variant: cpuCapability.String(), Variant: cpuCapability.String(),
ID: "0", ID: "0",
DependencyPath: depPath,
}, },
}, },
} }
@ -224,8 +227,6 @@ func GetGPUInfo() GpuInfoList {
return GpuInfoList{cpus[0].GpuInfo} return GpuInfoList{cpus[0].GpuInfo}
} }
depPath := LibraryDir()
// Load ALL libraries // Load ALL libraries
cHandles = initCudaHandles() cHandles = initCudaHandles()

10
llm/llm_windows.go
View file

@ -4,7 +4,10 @@ import (
"syscall" "syscall"
) )
const CREATE_DEFAULT_ERROR_MODE = 0x04000000 const (
CREATE_DEFAULT_ERROR_MODE = 0x04000000
ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
)
var LlamaServerSysProcAttr = &syscall.SysProcAttr{ var LlamaServerSysProcAttr = &syscall.SysProcAttr{
// Wire up the default error handling logic If for some reason a DLL is // Wire up the default error handling logic If for some reason a DLL is
@ -12,5 +15,8 @@ var LlamaServerSysProcAttr = &syscall.SysProcAttr{
// the user can either fix their PATH, or report a bug. Without this // the user can either fix their PATH, or report a bug. Without this
// setting, the process exits immediately with a generic exit status but no // setting, the process exits immediately with a generic exit status but no
// way to (easily) figure out what the actual missing DLL was. // way to (easily) figure out what the actual missing DLL was.
CreationFlags: CREATE_DEFAULT_ERROR_MODE, //
// Setting Above Normal priority class ensures when running as a "background service"
// with "programs" given best priority, we aren't starved of cpu cycles
CreationFlags: CREATE_DEFAULT_ERROR_MODE | ABOVE_NORMAL_PRIORITY_CLASS,
} }

2
macapp/src/app.tsx
View file

@ -19,7 +19,7 @@ export default function () {
const [step, setStep] = useState<Step>(Step.WELCOME) const [step, setStep] = useState<Step>(Step.WELCOME)
const [commandCopied, setCommandCopied] = useState<boolean>(false) const [commandCopied, setCommandCopied] = useState<boolean>(false)
const command = 'ollama run llama3.1' const command = 'ollama run llama3.2'
return ( return (
<div className='drag'> <div className='drag'>

4
server/images.go
View file

@ -1025,6 +1025,8 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
switch { switch {
case resp.StatusCode == http.StatusUnauthorized: case resp.StatusCode == http.StatusUnauthorized:
resp.Body.Close()
// Handle authentication error with one retry // Handle authentication error with one retry
challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate")) challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate"))
token, err := getAuthorizationToken(ctx, challenge) token, err := getAuthorizationToken(ctx, challenge)
@ -1040,8 +1042,10 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
} }
} }
case resp.StatusCode == http.StatusNotFound: case resp.StatusCode == http.StatusNotFound:
resp.Body.Close()
return nil, os.ErrNotExist return nil, os.ErrNotExist
case resp.StatusCode >= http.StatusBadRequest: case resp.StatusCode >= http.StatusBadRequest:
defer resp.Body.Close()
responseBody, err := io.ReadAll(resp.Body) responseBody, err := io.ReadAll(resp.Body)
if err != nil { if err != nil {
return nil, fmt.Errorf("%d: %s", resp.StatusCode, err) return nil, fmt.Errorf("%d: %s", resp.StatusCode, err)