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chore(autogptq): drop archived backend (#5214)

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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto 2025-04-19 15:52:29 +02:00 • committed by GitHub
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8
docs/content/docs/advanced/advanced-usage.md
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@ -268,14 +268,6 @@ yarn_ext_factor: 0
yarn_attn_factor: 0
yarn_beta_fast: 0
yarn_beta_slow: 0
# AutoGPT-Q settings, for configurations specific to GPT models.
autogptq:
model_base_name: "" # Base name of the model.
device: "" # Device to run the model on.
triton: false # Whether to use Triton Inference Server.
use_fast_tokenizer: false # Whether to use a fast tokenizer for quicker processing.
# configuration for diffusers model
diffusers:
cuda: false # Whether to use CUDA

1
docs/content/docs/features/GPU-acceleration.md
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@ -147,7 +147,6 @@ The devices in the following list have been tested with `hipblas` images running
| diffusers | yes | Radeon VII (gfx906) |
| piper | yes | Radeon VII (gfx906) |
| whisper | no | none |
| autogptq | no | none |
| bark | no | none |
| coqui | no | none |
| transformers | no | none |

42
docs/content/docs/features/text-generation.md
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@ -74,49 +74,9 @@ curl http://localhost:8080/v1/models
## Backends
### AutoGPTQ
[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) is an easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
#### Prerequisites
This is an extra backend - in the container images is already available and there is nothing to do for the setup.
If you are building LocalAI locally, you need to install [AutoGPTQ manually](https://github.com/PanQiWei/AutoGPTQ#quick-installation).
#### Model setup
The models are automatically downloaded from `huggingface` if not present the first time. It is possible to define models via `YAML` config file, or just by querying the endpoint with the `huggingface` repository model name. For example, create a `YAML` config file in `models/`:
```
name: orca
backend: autogptq
model_base_name: "orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order"
parameters:
model: "TheBloke/orca_mini_v2_13b-GPTQ"
# ...
```
Test with:
```bash
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "orca",
"messages": [{"role": "user", "content": "How are you?"}],
"temperature": 0.1
}'
```
### RWKV
A full example on how to run a rwkv model is in the [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/rwkv).
Note: rwkv models needs to specify the backend `rwkv` in the YAML config files and have an associated tokenizer along that needs to be provided with it:
```
36464540 -rw-r--r-- 1 mudler mudler 1.2G May 3 10:51 rwkv_small
36464543 -rw-r--r-- 1 mudler mudler 2.4M May 3 10:51 rwkv_small.tokenizer.json
```
RWKV support is available through llama.cpp (see below)
### llama.cpp