This PR updates `load_attention` to prefer loading specific attention
based on the model type. Additionally there were two cases where
`TensorParallelColumnLinear.load_multi` was called and this reduces it
to a single path
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Adds support for AMD Instinct MI300 in TGI.
Most changes are:
* Support PyTorch TunableOp to pick the GEMM/GEMV kernels for decoding
https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cuda/tunable.
TunableOp is disabled by default, and can be enabled with
`PYTORCH_TUNABLEOP_ENABLED=1`.
* Update ROCm dockerfile to PyTorch 2.3 (actually patched with changes
from https://github.com/pytorch/pytorch/pull/124362)
* Support SILU & Linear custom kernels contributed by AMD
* Update vLLM paged attention to https://github.com/fxmarty/rocm-vllm/,
branching out of a much more recent commit
3489ce7936
* Support FA2 Triton kernel as recommended by AMD. Can be used by
specifying `ROCM_USE_FLASH_ATTN_V2_TRITON=1`.
* Update dockerfile to ROCm 6.1
By default, TunableOp tuning results are saved in `/data` (e.g.
`/data/tunableop_meta-llama-Llama-2-70b-chat-hf_tp1_rank0.csv`) in order
to avoid to have to rerun the tuning at each `docker run`.
Example:
```
Validator,PT_VERSION,2.3.0
Validator,ROCM_VERSION,6.1.0.0-82-5fabb4c
Validator,HIPBLASLT_VERSION,0.7.0-1549b021
Validator,GCN_ARCH_NAME,gfx942:sramecc+:xnack-
Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty
GemmTunableOp_Half_TN,tn_8192_7_28672,Gemm_Rocblas_45475,0.132098
GemmTunableOp_Half_TN,tn_10240_4_8192,Gemm_Rocblas_45546,0.0484431
GemmTunableOp_Half_TN,tn_32000_6_8192,Default,0.149546
GemmTunableOp_Half_TN,tn_32000_3_8192,Gemm_Rocblas_45520,0.147119
GemmTunableOp_Half_TN,tn_8192_3_28672,Gemm_Rocblas_45475,0.132645
GemmTunableOp_Half_TN,tn_10240_3_8192,Gemm_Rocblas_45546,0.0482971
GemmTunableOp_Half_TN,tn_57344_5_8192,Gemm_Rocblas_45520,0.255694
GemmTunableOp_Half_TN,tn_10240_7_8192,Gemm_Rocblas_45517,0.0482522
GemmTunableOp_Half_TN,tn_8192_3_8192,Gemm_Rocblas_45546,0.0444671
GemmTunableOp_Half_TN,tn_8192_5_8192,Gemm_Rocblas_45546,0.0445834
GemmTunableOp_Half_TN,tn_57344_7_8192,Gemm_Rocblas_45520,0.25622
GemmTunableOp_Half_TN,tn_8192_2_28672,Gemm_Rocblas_45475,0.132122
GemmTunableOp_Half_TN,tn_8192_4_8192,Gemm_Rocblas_45517,0.0453191
GemmTunableOp_Half_TN,tn_10240_5_8192,Gemm_Rocblas_45517,0.0482514
GemmTunableOp_Half_TN,tn_8192_5_28672,Gemm_Rocblas_45542,0.133914
GemmTunableOp_Half_TN,tn_8192_2_8192,Gemm_Rocblas_45517,0.0446516
GemmTunableOp_Half_TN,tn_8192_1_28672,Gemm_Hipblaslt_TN_10814,0.131953
GemmTunableOp_Half_TN,tn_10240_2_8192,Gemm_Rocblas_45546,0.0481043
GemmTunableOp_Half_TN,tn_32000_4_8192,Gemm_Rocblas_45520,0.147497
GemmTunableOp_Half_TN,tn_8192_6_28672,Gemm_Rocblas_45529,0.134895
GemmTunableOp_Half_TN,tn_57344_2_8192,Gemm_Rocblas_45520,0.254716
GemmTunableOp_Half_TN,tn_57344_4_8192,Gemm_Rocblas_45520,0.255731
GemmTunableOp_Half_TN,tn_10240_6_8192,Gemm_Rocblas_45517,0.0484816
GemmTunableOp_Half_TN,tn_57344_3_8192,Gemm_Rocblas_45520,0.254701
GemmTunableOp_Half_TN,tn_8192_4_28672,Gemm_Rocblas_45475,0.132159
GemmTunableOp_Half_TN,tn_32000_2_8192,Default,0.147524
GemmTunableOp_Half_TN,tn_32000_5_8192,Default,0.147074
GemmTunableOp_Half_TN,tn_8192_6_8192,Gemm_Rocblas_45546,0.0454045
GemmTunableOp_Half_TN,tn_57344_6_8192,Gemm_Rocblas_45520,0.255582
GemmTunableOp_Half_TN,tn_32000_7_8192,Default,0.146705
GemmTunableOp_Half_TN,tn_8192_7_8192,Gemm_Rocblas_45546,0.0445489
```
---------
Co-authored-by: Mohit Sharma <mohit21sharma.ms@gmail.com>
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This PR adds paligemma modeling code
Blog post: https://huggingface.co/blog/paligemma
Transformers PR: https://github.com/huggingface/transformers/pull/30814
install the latest changes and run with
```bash
# get the weights
# text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf
# run TGI
text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf
```
basic example sending various requests
```python
from huggingface_hub import InferenceClient
client = InferenceClient("http://127.0.0.1:3000")
images = [
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png",
]
prompts = [
"What animal is in this image?",
"Name three colors in this image.",
"What are 10 colors in this image?",
"Where is the cow standing?",
"answer en Where is the cow standing?",
"Is there a bird in the image?",
"Is ther a cow in the image?",
"Is there a rabbit in the image?",
"how many birds are in the image?",
"how many rabbits are in the image?",
]
for img in images:
print(f"\nImage: {img.split('/')[-1]}")
for prompt in prompts:
inputs = f"{prompt}\n"
json_data = {
"inputs": inputs,
"parameters": {
"max_new_tokens": 30,
"do_sample": False,
},
}
generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False)
print([f"{prompt}\n{generated_output}"])
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).
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---------
Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>
# What does this PR do?
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Add's support for the Falcon2 11B model architecture.
## Before submitting
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other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
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---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: oOraph <13552058+oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: abhishek thakur <1183441+abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Co-authored-by: Christof Weickhardt <christof@weickhardt.ch>
Co-authored-by: Ikko Eltociear Ashimine <eltociear@gmail.com>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
Co-authored-by: Lucain <lucain@huggingface.co>
Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: Moritz Laurer <41862082+MoritzLaurer@users.noreply.github.com>
Co-authored-by: dr3s <dr3s@users.noreply.github.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Co-authored-by: Brandon Royal <2762697+brandonroyal@users.noreply.github.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
Co-authored-by: Martin Iglesias Goyanes <martinigoyanes@hotmail.com>
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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# What does this PR do?
- Changed all models to extract `embed_tokens` in order to enable llava
to separately call the embeddings and the core model layers.
- Added VlmCausalLM to inherit from FlashMistral in order to be
maximally supported. The only added logics sits on top and parses images
into pixel values, preallocates input_ids space for the image
embeddings, and passes them for the model.
- Added Clip for the vision tower.
- Didn't add flash for the vision tower since there's no padding anyway.
- Added heuristic (potentially incomplete) to calculate number of
features *before* calculating the clip patches (allows for easier logic
reuse of the LLM under the hood).
Still needs to be done:
- [x] Implement the image parsing in the controller side, to avoid
downloading n times per TP shard and also refusing requests too large
early and avoid issues where the truncation actually truncates the
image.
- [ ] Make sure it works with quantization properly.
- [x] Make sure it works with TP>1
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This PR adds `force_downcast_after` to `FastRMSNorm.forward` which is
used in the Gemma model. References
https://github.com/huggingface/transformers/pull/29402 and
https://github.com/huggingface/transformers/pull/29729
Setting `force_downcast_after=True` will perform the `hidden_states *
weight` multiplication in f32 and then downcast to half. This differs
slightly from the current implementation which first casts the
`hidden_states` to a half and then multiples.
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- Move float16 to bfloat16, which has less imprecisions (load test are
failing with the update kernels + f16, all working under bf16).
Another note, is that we are not respecting the layer norm in f32
defined in the configuration (this is OK in my book, but that could
impact the f16 precision)
- Moved to update kernels. Triton overhead is super high, removed by
switching to cuda graphs works great (update cuda graph is available
in TRT-LLM if needed, seems *exactly* like the regular ssm kernel.
- Moved inference_params struct in order to make only 2 tensors, to
reduce the overhead of copying back and forth to the cuda graphs.
- Left over overhead seems entirely in the tokenization bit. (Still 4
copies are paid before launching the graph)
# What does this PR do?
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This PR adds the possibility to run AWQ models with Exllama/GPTQ
kernels, specifically for ROCm devices that support Exllama kernels but
not AWQ's GEMM.
This is done by :
- un-packing, reordering and re-packing AWQ weights when `--quantize
gptq` but the model's `quant_method=awq`.
- avoiding overflows when adding 1 to zeros in exllama and triton.
Ref: https://github.com/casper-hansen/AutoAWQ/pull/313
## Before submitting
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other checks if that's the case).
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This draft PR is a work in progress implementation of the mamba model.
This PR currently loads weights, and produces correct logits after a
single pass.
This PR still needs to correctly integrate this model so it produces
tokens as expected, and apply optimization to avoid all copies during
runtime/unnecessary operations.
#### Helpful resources
[Mamba: Linear-Time Sequence Modeling with Selective State Spaces
(Albert Gu and Tri Dao)](https://arxiv.org/abs/2312.00752)
https://github.com/johnma2006/mamba-minimalhttps://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rshttps://github.com/huggingface/transformers/pull/28094
Notes: this dev work is currently targeting `state-spaces/mamba-130m`,
so if you want to test please use that model. Additionally when starting
the router the prefill needs to be limited: `cargo run --
--max-batch-prefill-tokens 768 --max-input-length 768`
## Update / Current State
Integration tests have been added and basic functionality such as model
loading is supported.
```bash
cd integration-tests
pytest -vv models/test_fused_kernel_mamba.py
```
- [x] add tests
- [x] load model
- [x] make simple request
- [ ] resolve warmup issue
- [ ] resolve output issues
fetching models tested during dev
```bash
text-generation-server download-weights state-spaces/mamba-130m
text-generation-server download-weights state-spaces/mamba-1.4b
text-generation-server download-weights state-spaces/mamba-2.8b
```
The server can be run
```bash
cd server
MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8b
```
router
```bash
cargo run
```
make a request
```bash
curl -s localhost:3000/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json' | jq
```
response
```json
{
"generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data."
}
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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---------
Co-authored-by: Choon Meng Tan <choonmeng@aisingapore.org>
Co-authored-by: David Ong Tat-Wee <13075447+ongtw@users.noreply.github.com>
This PR adds basic modeling for phi-2
run
```bash
text-generation-server \
serve \
microsoft/phi-2 \
--revision 834565c23f9b28b96ccbeabe614dd906b6db551a
```
test
```bash
curl -s localhost:3000/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json' | jq .
# {
# "generated_text": "\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from data. These"
# }
```
notes
- recently (~1 day ago) the Phi weights and model were updated to
accommodate adding [GQA/MQA attention to the
model.](https://github.com/huggingface/transformers/pull/28163) This
impl expects the original model format so a fixed revision is required
at the moment.
- this PR only includes a basic implementation of the model and can
later be extended for support Flash and Sharded versions as well as make
use of better optimization
This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.
Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?
---------
Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>
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# What does this PR do?
Fix#1095
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# What does this PR do?
This PR is meant to handle cases in which the images provided are
invalid.
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@Narsil
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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`FastLinear.load` requires 4 parameters, but in the following only 3 are
given. This PR fix this.
```python
# server/text_generation_server/models/custom_modeling/opt_modeling.py
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = FastLinear.load(
config, prefix="model.decoder.project_out", bias=False
)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = FastLinear.load(
config, prefix="model.decoder.project_in", bias=False
```
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# What does this PR do?
Fixes#1056
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# What does this PR do?
Fixes#1038
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if there's no cuda. disable custom kernels
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.
By the way, the changes of this time mainly for supporting Baichuan-7B.
---------
Co-authored-by: xiaoyuze <xiaoyuze@baichuan.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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Fixes # (issue)
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Co-Authored-By: Victor Sanh <victorsanh@gmail.com>
# What does this PR do?
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Fixes # (issue)
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---------
Co-authored-by: Victor Sanh <victorsanh@gmail.com>
# What does this PR do?
Reflecting
https://github.com/huggingface/transformers/pull/24998
Current status wants to make sure integration tests *are* broken with
this.
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# What does this PR do?
Fixes#749
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Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>
# What does this PR do?
Fixed typo.
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implemetation -> implementation
## Before submitting
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# What does this PR do?
- Adds Rope NTK scaling.
Done because
https://github.com/huggingface/text-generation-inference/pull/529 was
closed
Took some code from
https://github.com/huggingface/transformers/pull/24653
- `--rope-scaling` and `--rope-factor` are added separately. I
considered having a single one and parsing something line ("linear:4.0"
, or "dynamic") but decided against
it because it would push more parsing+validation a bit everywhere (both
in the launcher and the server).
Fixes#512
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- Current PR is not great because we're side stepping the
`Weights.__init__` but Weights shouldn't requires anything related
to the config or the model_id as it aims to be a simple Wrapper
over multi file loading.
- Ideal solution would be to use something like Rust enum
```
enum Quantize{
Bitandbytes(Bitsandbytes),
GPTQ(bits: usize, groupsize: usize)
```
And passing that around during load. Unfortunately we don't
have access to this, so for now, side-stepping seems easier.
- Re-enabling groupsize<0 with exllama (confirmed it works.)
Helps #601
In next steps we should make sure our quantization script uses that
format and make it standard.
# What does this PR do?
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Just trying to get the integration tests to pass.
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---------
Co-authored-by: Felix Marty <9808326+fxmarty@users.noreply.github.com>
# What does this PR do?
Some models are already converted, and do not have those values in the
file, this enables users to use them with less friction.
Went for pure env based because adding flags would end up (imo) very
tedious to maintain. There's a lot of sanitation to do: those flags
would be errors if not used in conjuction with `--quantize gptq`.
Then the flags need to exist in the launcher and the server passing them
all throughout all function calls.
This PR is intended as an easy escape hatch, not the defacto method to
use gptq in TGI.
Fixes#500
Let's start discussing implementation.
- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).
Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.
My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.
# What does this PR do?
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---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
# What does this PR do?
Reworked the loading logic. Idea is to use cleaner loading code:
- Remove need for `no_init_weights`
- Remove all weird `bnb_linear` and `load_weights` and
`post_load_weights`.
New code layout:
- New class `Weights` in charge of handling loading the weights from
multiple files into appropiate tensors (potentially sharded)
- TP layers now are "shells", they contain the code to know what kind of
sharding we need + eventual `all_reduce`. They do not inherit from
linear, but they contain some kind of Linear instead
- the contained linear can be either FastLinear, BnbLinear or GPTq
Linear next.
- All modeling code is explictly made for sharding, process group is
just no-ops for non sharded code (removes a lot of test cases)
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
# What does this PR do?
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Changes only the type from `bool` to `Option<Enum>` pretty much
everywhere.
- Use `Optional[str]` in Python (easier to manage than importing type
everywhere). Except for the cli to get proper validation
- Updated all models to handle gracefully new values. (Error out if
unknown value, or gptq since not implemented).
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