Fix flash-attn install

README.md

@@ -46,9 +46,11 @@ Then download this repository and install its requirements:
 pip install -U huggingface_hub
 hf download HuggingFaceBio/Carbon-A-1.2B --exclude model.safetensors --local-dir Carbon-A-1.2B
 pip install -r Carbon-A-1.2B/requirements.txt
-pip install --no-build-isolation flash-attn
+pip install --no-build-isolation --no-cache-dir "flash-attn==2.8.3"
 ```
 
+The requirements keep PyTorch below 2.9 because FlashAttention 2.8.3 has prebuilt wheels for these versions on Python 3.9–3.13; without a wheel, it compiles from source, which fails with recent PyTorch. PyTorch's default CUDA 12.8 build also runs with CUDA 13 drivers. To use a newer PyTorch, run without FlashAttention with `--attn-implementation sdpa`. Its probabilities differ slightly: on the yeast benchmark below, PyTorch 2.14 with SDPA gave a gene F1 of 0.9525 instead of 0.953.
+
 The weights (4.6 GB) are downloaded to the Hugging Face cache on first use.
 
 ### Annotate a genome

annotation_pipeline/README.md

@@ -57,9 +57,11 @@ FlashAttention:
 pip install -U "huggingface_hub>=0.34"
 hf download HuggingFaceBio/Carbon-A-1.2B --exclude model.safetensors --local-dir Carbon-A-1.2B
 pip install -r Carbon-A-1.2B/requirements.txt
-pip install --no-build-isolation flash-attn
+pip install --no-build-isolation --no-cache-dir "flash-attn==2.8.3"
 ```
 
+The requirements keep PyTorch below 2.9 because FlashAttention 2.8.3 has prebuilt wheels for these versions on Python 3.9–3.13; without a wheel, it compiles from source, which fails with recent PyTorch. PyTorch's default CUDA 12.8 build also runs with CUDA 13 drivers. To use a newer PyTorch, run without FlashAttention with `--attn-implementation sdpa`. Its probabilities differ slightly: on the yeast benchmark of the model card, PyTorch 2.14 with SDPA gave a gene F1 of 0.9525 instead of 0.953.
+
 Model inference loads Carbon-A with `from_pretrained`, which downloads the
 weights (4.6 GB) to the Hugging Face cache on first use. It runs in bfloat16
 with FlashAttention-2, the setting of all reported results. The requirements

requirements.txt

@@ -1,4 +1,6 @@
-torch>=2.7,<3
+# Below 2.9: flash-attn 2.8.3 ships prebuilt wheels for these PyTorch versions
+# on Python 3.9-3.13.
+torch>=2.7,<2.9
 # Below 5: Transformers 5 does not load this model with flash_attention_2, the
 # annotation pipeline's default attention implementation.
 transformers>=4.56,<5
@@ -14,7 +16,9 @@ tqdm
 numba
 ninja
 # Its default attention implementation, flash_attention_2, also needs FlashAttention:
-# pip install --no-build-isolation flash-attn
+# pip install --no-build-isolation --no-cache-dir "flash-attn==2.8.3"
 # (--attn-implementation sdpa runs without it.) The native Viterbi backends are
 # compiled on first use: the CPU backend needs a C++17 compiler with OpenMP and
 # the GPU backend a CUDA toolkit.
+# Needed to install flash-attn:
+psutil