Bump the pip group across 3 directories with 1 update

[dependabot]

Bumps the pip group with 1 update in the /examples/AzureMLSeedCls directory: transformers.
Bumps the pip group with 1 update in the /examples/Flask directory: transformers.
Bumps the pip group with 1 update in the /examples/SeedDetector directory: transformers.

Updates transformers from 4.35.0 to 4.38.0

Release notes

Sourced from transformers's releases.

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

• Flash Attention 2

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2"
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

• bitsandbytes-4bit

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", load_in_4bit=True
)
</tr></table>

... (truncated)

Commits

• 08ab54a [ gemma] Adds support for Gemma 💎 (#29167)
• 2de9314 [Maskformer] safely get backbone config (#29166)
• 476957b 🚨 Llama: update rope scaling to match static cache changes (#29143)
• 7a4bec6 Release: 4.38.0
• ee3af60 Add support for fine-tuning CLIP-like models using contrastive-image-text exa...
• 0996a10 Revert low cpu mem tie weights (#29135)
• 15cfe38 [Core tokenization] add_dummy_prefix_space option to help with latest is...
• efdd436 FIX [PEFT / Trainer ] Handle better peft + quantized compiled models (#29...
• 5e95dca [cuda kernels] only compile them when initializing (#29133)
• a7755d2 Generate: unset GenerationConfig parameters do not raise warning (#29119)
• Additional commits viewable in compare view

Updates transformers from 4.36.0 to 4.38.0

Release notes

Sourced from transformers's releases.

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

• Flash Attention 2

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2"
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

• bitsandbytes-4bit

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", load_in_4bit=True
)
</tr></table>

... (truncated)

Commits

• 08ab54a [ gemma] Adds support for Gemma 💎 (#29167)
• 2de9314 [Maskformer] safely get backbone config (#29166)
• 476957b 🚨 Llama: update rope scaling to match static cache changes (#29143)
• 7a4bec6 Release: 4.38.0
• ee3af60 Add support for fine-tuning CLIP-like models using contrastive-image-text exa...
• 0996a10 Revert low cpu mem tie weights (#29135)
• 15cfe38 [Core tokenization] add_dummy_prefix_space option to help with latest is...
• efdd436 FIX [PEFT / Trainer ] Handle better peft + quantized compiled models (#29...
• 5e95dca [cuda kernels] only compile them when initializing (#29133)
• a7755d2 Generate: unset GenerationConfig parameters do not raise warning (#29119)
• Additional commits viewable in compare view

Updates transformers from 4.36.0 to 4.38.0

Release notes

Sourced from transformers's releases.

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

• Flash Attention 2

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2"
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)

• bitsandbytes-4bit

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b", device_map="auto", load_in_4bit=True
)
</tr></table>

... (truncated)

Commits

• 08ab54a [ gemma] Adds support for Gemma 💎 (#29167)
• 2de9314 [Maskformer] safely get backbone config (#29166)
• 476957b 🚨 Llama: update rope scaling to match static cache changes (#29143)
• 7a4bec6 Release: 4.38.0
• ee3af60 Add support for fine-tuning CLIP-like models using contrastive-image-text exa...
• 0996a10 Revert low cpu mem tie weights (#29135)
• 15cfe38 [Core tokenization] add_dummy_prefix_space option to help with latest is...
• efdd436 FIX [PEFT / Trainer ] Handle better peft + quantized compiled models (#29...
• 5e95dca [cuda kernels] only compile them when initializing (#29133)
• a7755d2 Generate: unset GenerationConfig parameters do not raise warning (#29119)
• Additional commits viewable in compare view

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