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setup page tweaks

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Alex O'Connell
2025-10-25 23:08:31 -04:00
parent 0206673303
commit 5429d72aee
1 changed files with 11 additions and 6 deletions
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docs/Setup.md
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@@ -70,7 +70,7 @@ Pressing `Submit` will download the model from HuggingFace. The downloaded files
### Step 3: Model Configuration
This step allows you to configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.
For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Home-LLM (v1-v3)` API. This API is included to ensure compatability with the Home-LLM models that were trained before the introduction of the built in Home Assistant LLM API.
For now, defaults for the model should have been populated. If you would like the Home-LLM model to be able to control devices then you should select the `Home-LLM (v1-v3)` API. This API is included to ensure compatability with the Home-LLM models that were trained before the introduction of the built in Home Assistant LLM API.
Once the desired API has been selected, scroll to the bottom and click `Submit`.
@@ -93,8 +93,13 @@ There are multiple size options for the Qwen3 series of model. Replace `8b` with
Qwen3 can be easily set up and downloaded on the serving machine using the `ollama pull qwen3:8b` command.
In order to access the model from another machine, we need to run the Ollama API server open to the local network. This can be achieved using the `OLLAMA_HOST=0.0.0.0:11434 ollama serve` command. **DO NOT RUN THIS COMMAND ON ANY PUBLICLY
ACCESSIBLE SERVERS AS IT LISTENS ON ALL NETWORK INTERFACES**
> Note: You can also host the Home-LLM models on Ollama by pulling them from HuggingFace directly by prepending `hf.co/` to the full model name. For example:
> - `acon96/Home-3B-v3-GGUF` -> `ollama pull hf.co/acon96/Home-3B-v3-GGUF`
> - `acon96/Home-1B-v3-GGUF` -> `ollama pull hf.co/acon96/Home-1B-v3-GGUF`
In order to access the model from another machine, we need to run the Ollama API server open to the local network. This can be achieved using the `OLLAMA_HOST=0.0.0.0:11434 ollama serve` command.
**DO NOT RUN THIS COMMAND ON ANY PUBLICLY ACCESSIBLE SERVERS AS IT LISTENS ON ALL NETWORK INTERFACES**
### Step 2: Connect to the Ollama API
@@ -112,11 +117,11 @@ In order to access the model from another machine, we need to run the Ollama API
### Step 3: Model Selection & Configuration
1. You must create the conversation agent based on the model you wish to use.
Under the `Ollama at '<url>` service that you just created, select `+ Add conversation agent`
Under the `Ollama at '<url>'` service that you just created, select `+ Add conversation agent`
- **Model Name**: Select `qwen3:8b` from the list.
2. You can configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.
For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Assist` API.
For now, defaults for the model should have been populated. If you would like the Qwen3 model to be able to control devices then you should select the `Assist` API. This is the included Home Assistant API for controlling devices via Large Language Models.
Once the desired model has been selected & configured, scroll to the bottom and click `Submit`.
@@ -154,7 +159,7 @@ Llama 3 8B can be set up and downloaded on the serving machine using LM Studio b
- - **Model Name**: Set this to the name of the model as it appears in LM Studio. The dropdown list should pre-populate with the models that are already installed.
2. You can configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.
For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Assist` API.
For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you should select the `Assist` API. This is the included Home Assistant API for controlling devices via Large Language Models.
> NOTE: The key settings in this case are that our prompt references the `{{ response_examples }}` variable and the `Enable in context learning (ICL) examples` option is turned on.