* refine get xpu free memory
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* enable qwen2 in xpu
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* enable gemma/gemma2/phi in intel platform
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* feat: first draft load multiple lora
* feat: load weights within layer and refactor lora pass
* fix: refactor and reduce lora math
* feat: baseline impl single request multi lora support
* feat: prefer lorax implementation and port loading logic
* fix: prefer adapter_data and refactors
* feat: perfer loraxs custom punica kernels and add mlp loras
* fix: adjust batch for bgmv
* fix: adjust adapter_segments logic when in batch
* fix: refactor and move changes to v3 proto
* fix: pass model_id for all flash causal lms
* fix: pass model_id for all causal and seq2seq lms
* fix: add model_id to model test
* feat: add lora support to mistral and refactors
* feat: prefer model id in request
* fix: include rust code for adapter id
* feat: bump launcher and add new lora docs
* feat: support base model generation and refactors
* fix: rename doc to retry ci build
* feat: support if vlm models
* fix: add adapter_data param and avoid missing layers
* fix: add adapter_data param to phi and neox
* fix: update all models forwards to include adapter_data
* fix: add model_id to IdeficsCausalLM
* Update lora.md
Fixed a typo
* Update lora.md
Fixing spam image
* fix: add lora kernel to dockerfile, support running without kernels and refactors
* fix: avoid dockerfile conflict
* fix: refactors and adjust flash llama lora logic
* fix: skip llama test due to CI issue (temp)
* fix: skip llama test CI (temp) 2
* fix: revert skips and prefer updated ci token for tests
* fix: refactors and helpful comments
* fix: add noop in TensorParallelAdapterRowLinear too
* fix: refactor and move shard_lora_weights logic
* fix: exit early if no adapter_data
---------
Co-authored-by: Derek <datavistics@gmail.com>
Add support for GPTQ Marlin kernels
GPTQ Marlin extends the Marlin kernels to support common GPTQ
configurations:
- bits: 4 or 8
- groupsize: -1, 32, 64, or 128
- desc_act: true/false
Using the GPTQ Marlin kernels requires repacking the parameters in the
Marlin quantizer format.
The kernels were contributed by Neural Magic to VLLM. We vendor them
here for convenience.
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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---------
Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>
# What does this PR do?
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[forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
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Here are the
[documentation
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[here are tips on formatting
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Wang, Yi
