323da791eb
* bump transformers to 5.5.4 and trl to latest 1.1.0 * more upgrades * update peft too * adapt lora_merge to peft 0.19 layer config API PEFT 0.19 requires a LoraConfig object on Linear/ParamWrapper/Conv layer constructors and moved use_rslora, use_dora, fan_in_fan_out, lora_dropout, and lora_bias into that config. Build the config per branch in _build_peft_layer_and_get_delta so the merge utility works with the upgraded peft. * allow lora_dropout on mixed attention+MoE configs under peft 0.19 PEFT 0.19's convert_peft_config_for_transformers auto-remaps old MoE target_modules (w1/w2/w3 on Mixtral, etc.) into target_parameters for transformers v5's fused 3D expert Parameters. Those targets get wrapped with ParamWrapper, which rejects lora_dropout != 0 because the 3D einsum can't factor dropout out of lora_B(lora_A(dropout(x))). Monkeypatch ParamWrapper.__init__ to internally use a copy of the LoraConfig with lora_dropout=0, so its dropout slot becomes nn.Identity while the shared config still delivers real dropout to sibling Linear LoRA layers (attention q/k/v/o). A probe runs the same conversion on a deep copy to detect the situation and emit a warning before patching.
79 lines
1.2 KiB
Plaintext
79 lines
1.2 KiB
Plaintext
--extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
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# START section of dependencies that don't install on Darwin/MacOS
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bitsandbytes==0.49.1
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triton>=3.4.0
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mamba-ssm==1.2.0.post1
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xformers>=0.0.23.post1
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liger-kernel==0.7.0
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# END section
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packaging==26.0
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huggingface_hub>=1.1.7
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peft>=0.19.0,<0.20.0
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tokenizers>=0.22.1
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transformers==5.5.4
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accelerate==1.13.0
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datasets>=4.8.4,<4.9.0
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deepspeed>=0.18.6,<0.19.0
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trl==1.1.0
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hf_xet==1.4.3
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kernels==0.13.0
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fla-core==0.4.1
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flash-linear-attention==0.4.1
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trackio>=0.16.1
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typing-extensions>=4.15.0
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optimum==1.16.2
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hf_transfer
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sentencepiece
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gradio>=6.2.0,<7.0
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modal==1.3.0.post1
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pydantic>=2.10.6
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addict
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fire
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PyYAML>=6.0
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requests
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wandb
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einops
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colorama
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numba>=0.61.2
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numpy>=2.2.6
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# qlora things
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evaluate==0.4.1
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scipy
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nvidia-ml-py==12.560.30
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art
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tensorboard
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python-dotenv==1.0.1
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# remote filesystems
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s3fs>=2024.5.0
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gcsfs>=2025.3.0
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adlfs>=2024.5.0
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ocifs==1.3.2
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zstandard==0.22.0
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fastcore
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# lm eval harness
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lm_eval==0.4.11
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langdetect==1.0.9
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immutabledict==4.2.0
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antlr4-python3-runtime==4.13.2
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torchao==0.17.0
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openenv-core==0.1.0
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schedulefree==1.4.1
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axolotl-contribs-lgpl==0.0.7
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axolotl-contribs-mit==0.0.6
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# telemetry
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posthog==6.7.11
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mistral-common==1.11.0
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