chore: update docker docs (#3623)

* fix: uv leftover docs

* fix: docker build failing

* chore: doc

* fix: remove old pytorch build

* fix: stop recommend flash-attn optional, let transformers pull

* fix: remove ring flash attention from image

* fix: quotes [skip ci]

* chore: naming [skip ci]

.github/CONTRIBUTING.md

@@ -31,10 +31,11 @@ PRs are **greatly welcome**!
 
 Please run below to setup env
 ```bash
-# Install axolotl + dev and test dependencies from lockfile
+# Install axolotl + dev and test dependencies
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv sync --extra flash-attn --extra deepspeed --group dev --group test
+uv venv --no-project --relocatable
 source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 pre-commit install
 
 # test

.github/workflows/base.yml

@@ -30,14 +30,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -168,14 +160,6 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""

.github/workflows/main.yml

@@ -18,12 +18,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -180,12 +174,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"

ATTN_REFACTOR_REVIEW.md

@@ -1,142 +0,0 @@
-# `attn-implementation-refactor` branch review
-
-Review target: `attn-implementation-refactor` (5 commits ahead of main, merge base `69904781`).
-Scope: 16 files, +682 / −96.
-
-## 1. What the branch is trying to do
-
-Collapse seven boolean attention flags (`flash_attention`, `sdp_attention`, `xformers_attention`, `flex_attention`, `sage_attention`, `s2_attention`, `eager_attention`) into a single `attn_implementation` field, with derived capability flags (`attn_supports_packing`, `attn_uses_flash_lib`, `attn_needs_dtype_cast`) for the gates that used to be ad-hoc OR-chains.
-
-Mechanism: `normalize_attn_implementation` (a `@model_validator(mode="before")` on `AxolotlInputConfig`) maps bidirectionally between the new field and the legacy flags, with a priority list for legacy combos (`s2 + flash → s2`), and then computes the three capability flags from frozen sets in `enums.py`.
-
-Adjacent changes: `xformers` and `sage` now register as their own entries in `ALL_ATTENTION_FUNCTIONS` (with FA2 mask behavior) instead of stomping the `flash_attention_2` slot. New `fp8` backend wires `torchao.prototype.attention.apply_low_precision_attention` in `apply_post_model_load_patches`.
-
-## 2. Target design
-
-**`cfg.attn_implementation` is the single source of truth on the validated config.**
-
-- Its type is `str | None`. Accepted values are **canonical names only** — no short-form aliases:
-  - HF-native: `eager`, `sdpa`, `flash_attention_2`, `flash_attention_3`, `flex_attention`. (`flash_attention_3` is net-new to axolotl — the current branch only encodes `flash_attention_2` under the short name `flash`.)
-  - Axolotl-owned (registered into `ALL_ATTENTION_FUNCTIONS` under exactly these names): `xformers`, `sage`, `s2`, `fp8`.
-  - Hub-kernel paths: `kernels-community/sage-attention`, `kernels-community/flash-attn3`, etc. — passthrough. Known-kernel allowlist in `enums.py` classifies the common ones into the capability tables.
-  Short forms like `flash`, `fa2`, `fa3`, `sdp`, `flex` are rejected (Pydantic validation error with a pointer to the canonical name).
-- `model.py:_set_attention_config` passes `cfg.attn_implementation` to HF verbatim — no `_ATTN_IMPL_TO_HF` translation dict needed.
-- Legacy booleans (`flash_attention: true`, `sdp_attention: true`, …) are the **only** input aliases, kept for backwards compatibility. The normalizer maps them to the canonical `attn_implementation` value, emits a one-time `DeprecationWarning` per flag, and removes them from `data` so they're never readable on the validated `cfg`. `deprecated=True` on each Field surfaces this in JSON schema. Mapping is 1:1 with the current legacy-flag semantics (`flash_attention → flash_attention_2`, `sdp_attention → sdpa`, `flex_attention → flex_attention`, `xformers_attention → xformers`, `sage_attention → sage`, `s2_attention → s2`, `eager_attention → eager`).
-- Capability flags (`attn_supports_packing`, `attn_uses_flash_lib`, `attn_needs_dtype_cast`) are **`@computed_field` on the model**, not settable inputs. Lookup is keyed by the canonical `attn_implementation` string.
-- Unknown / user-supplied strings (custom hub kernels) pass through to HF but get **conservative capability defaults** (packing=False, flash-lib=False, dtype-cast=True). Known hub kernels axolotl can classify live in a small allowlist.
-- Downstream consumers read *only* `cfg.attn_implementation` and the capability flags. No `cfg.flash_attention`, `cfg.xformers_attention`, etc. anywhere in `src/`.
-
-This is strictly what the branch is already *trying* to do — the gaps below are places it hasn't landed that goal yet.
-
-## 3. Gaps and holes
-
-### A. Legacy flags are still parallel state, not input-only
-
-1. The normalizer *sets* the legacy flags on `data` (`impl_to_flag[attn_impl]` branch). It does not delete them. So `cfg.flash_attention` is still truthy after validation, and downstream code still reads it (see G).
-2. Short-form enum values (`flash`, `sdpa`, `fp8`) are persisted as-is on `cfg.attn_implementation`, which is why `model.py` needs `_ATTN_IMPL_TO_HF` to translate before passing to HF. Source-of-truth implies canonicalize at normalize-time, not translate at consume-time.
-3. Legacy flag + `attn_implementation` (consistent combo, e.g. `attn_implementation: flash + flash_attention: true`) emits no deprecation warning — only legacy-only path warns.
-4. Legacy Field descriptions (`xformers_attention`, `sdp_attention`, etc.) don't have `deprecated=True`, so JSON schema still advertises them as first-class.
-
-### B. Validators that still only check the legacy flag
-
-5. **`check_ebft_activation_offloading`** (`validation.py:1607-1619`) reads only `data.get("flex_attention")`. Users on `attn_implementation: flex_attention` bypass the incompatibility check.
-6. **`check_sample_packing_without_attention`** (`validation.py:188-203`) early-returns when `attn_implementation` is set but never validates the chosen backend actually supports packing. `attn_implementation: eager + sample_packing: true` silently passes; the old legacy-flag check warned.
-
-### C. Non-enum strings fall through the capability tables
-
-7. **HF-native `"flash_attention_2"`** is neither in `impl_to_flag` nor `FLASH_ATTN_LIB_IMPLS`. A user copy-pasting from HF docs gets `attn_uses_flash_lib=False`, silently disabling FA4 auto-apply, LLaMA flash hijack, `_patch_attention` (btlm, stablelm_epoch, mistral3, llava), and `_apply_flash_attention_peft_patches`.
-8. **Hub kernel strings** (`kernels-community/flash-attn3`, `kernels-community/sage-attention`) default to `attn_supports_packing=True` (silently enters multipack with varlen `position_ids` — correctness depends on the kernel honoring them) and `attn_uses_flash_lib=False` (so `context_parallel_size > 1` raises "requires flash attention" even for FA3 hub kernels).
-9. **Conflict trap for hub-kernel + legacy flag** (`config.py:1414-1419`): `attn_implementation: kernels-community/flash-attn3 + flash_attention: true` always raises, because `impl_to_flag.get(custom) is None` and the loop treats `flag != None` as conflict. Common combo in existing YAMLs breaks hard on upgrade.
-
-### D. Silent behaviour change for xformers
-
-10. Old `_apply_flash_attention_patches` did `self.cfg.flash_attention = True` for `xformers + sample_packing`. The new version doesn't, and xformers is not in `FLASH_ATTN_LIB_IMPLS`. Consumers that keyed off `cfg.flash_attention` now see falsy for xformers, silently dropping `_patch_attention` (btlm / stablelm_epoch+packing / mistral3 / llava model-type FA patches). Some of this is arguably correct cleanup (xformers has its own HF registry entry now), but the btlm/stablelm/mistral3 regression is not called out and not tested. Decide consciously, not by omission.
-
-### E. Capability flags are writable Pydantic fields, not computed
-
-11. `attn_supports_packing`, `attn_uses_flash_lib`, `attn_needs_dtype_cast` are declared `bool | None = Field(default=None)` on `AxolotlInputConfig`. YAML is not rejected — a user can set `attn_uses_flash_lib: true` and override the normalizer.
-
-### F. Validator ordering (not covered by tests)
-
-12. `AttentionValidationMixin.check_attention_fields` (inherited, `mode="before"`) and `normalize_attn_implementation` (subclass, `mode="before"`) both run during `model_validator` phase. Pydantic MRO may run the inherited one first. For legacy-only `s2_attention: true + flash_attention: true` (the test `test_s2_plus_flash_maps_to_s2` asserts this maps to `s2`), the inherited check may raise "multiple attention implementations set" before the normalizer runs. The test calls the classmethod directly and does not build the model, so this is unverified either way.
-
-### G. Remaining legacy reads in `src/`
-
-13. `src/axolotl/integrations/lm_eval/cli.py:120` reads `cfg.flash_attention`. Works for `attn_implementation=flash` only.
-14. `tests/e2e/multigpu/test_llama.py:524-526` writes `cfg.flash_attention = True` / `cfg.flex_attention = True`. Stale pattern.
-15. Dual-check idioms in `config.py` (lines 1464, 1478, 1570, 1586, 1774) and `validation.py` (198, 209, 221, 850, 1586, 1611) — `data.get("x_attention") or data.get("attn_implementation") == "x"` — are redundant once legacy flags are input-only; remove them.
-
-### H. fp8 operational risk
-
-16. The `fp8` docstring documents hard requirements (PyTorch ≥ 2.11, SM90+, flash-attn with FA3, torchao ≥ 0.17.0) and a runtime constraint (`config.use_cache = False`). None are validated — misconfig surfaces as a torchao runtime error. `xformers` and `sage` availability/compute-capability guards exist; `fp8` should match.
-
-### I. Test coverage gaps
-
-17. `test_attn_implementation.py` exercises the classmethod in isolation plus the constant sets. It does **not**:
-   - Build a full `AxolotlInputConfig(**data)` (so validator ordering — item 12 — is untested).
-   - Verify capability flags can't be overridden from YAML (item 11).
-   - Cover `check_sample_packing_without_attention` with `attn_implementation: eager` (item 6).
-   - Cover `check_ebft_activation_offloading` with `attn_implementation: flex_attention` (item 5).
-   - Cover hub-kernel + legacy flag combo (item 9).
-   - Cover `flash_attention_2` canonicalization (item 7).
-
-## 4. Fix plan
-
-Four phases, each commit-sized. Phases 1–2 are local and low-risk; phase 3 is the behaviour-changing cleanup; phase 4 is tests + docs.
-
-### Phase 1 — Lock down the data model
-
-1. Drop the `AttnImplementation` enum. `attn_implementation` becomes `str | None`, validated against a canonical allowlist (`eager`, `sdpa`, `flash_attention_2`, `flash_attention_3`, `flex_attention`, `xformers`, `sage`, `s2`, `fp8`) **or** a hub-kernel path (`startswith("kernels-")` / contains `/`). Reject short-form strings like `flash` / `fa2` / `sdp` / `flex` with an explicit error pointing at the canonical name.
-2. Rewrite `normalize_attn_implementation` so its only job is mapping **legacy booleans → canonical `attn_implementation`** (for BC). Mapping is fixed:
-   - `flash_attention → flash_attention_2`
-   - `sdp_attention → sdpa`
-   - `flex_attention → flex_attention`
-   - `xformers_attention → xformers`
-   - `sage_attention → sage`
-   - `s2_attention → s2`
-   - `eager_attention → eager`
-   Priority for legacy combos stays as in the current branch (`s2 > sage > xformers > flex > flash > sdp > eager`). Emit a one-time `DeprecationWarning` per unique legacy flag seen. After mapping, delete the legacy flag keys from `data` so they never appear on validated `cfg`. If both a canonical `attn_implementation` and any legacy flag are set, raise (no silent precedence).
-
-   **Merge `AttentionValidationMixin.check_attention_fields` into this normalizer and delete the mixin method.** Pydantic v2 runs inherited `mode="before"` validators before subclass ones per MRO, so leaving them as siblings causes the inherited check to reject legacy combos like `s2 + flash` before the normalizer can map them. One validator, one source of conflict detection.
-
-   **Fix the gemma4-hybrid path**: change `data["attn_implementation"] = "flash"` to `data["attn_implementation"] = "flash_attention_2"` (the short name no longer validates after step 1).
-3. Convert `attn_supports_packing`, `attn_uses_flash_lib`, `attn_needs_dtype_cast` to `@computed_field`. The three capability tables move to `enums.py` as module constants keyed by the canonical `attn_implementation` string (including `flash_attention_3` — missing from the current branch — and known hub kernels):
-   - Packing-capable: `{flash_attention_2, flash_attention_3, flex_attention, xformers, sage, kernels-community/flash-attn3, kernels-community/sage-attention}`.
-   - Flash-lib (axolotl's monkeypatch targets): `{flash_attention_2, flash_attention_3, s2, kernels-community/flash-attn3}`.
-   - No-dtype-cast: `{eager, sdpa}`.
-   Unknown strings: conservative defaults (`packing=False, flash_lib=False, dtype_cast=True`).
-4. Delete `_ATTN_IMPL_TO_HF` from `model.py` and pass `cfg.attn_implementation` straight through. The gemma4-hybrid branch continues to override to `flash_attention_2` before passing to HF.
-5. `deprecated=True` on each legacy boolean Field so JSON schema + Pydantic surface the deprecation.
-
-### Phase 2 — Fix the validators
-
-6. `check_sample_packing_without_attention`: drop the early-return and gate on `attn_supports_packing`. Warn (or raise — pick one and be consistent) if packing is enabled with a non-packing backend.
-7. `check_ebft_activation_offloading`: replace `data.get("flex_attention")` with `attn_implementation == "flex_attention"`.
-8. Sweep items (item 15): remove every `data.get("x_attention") or data.get("attn_implementation") == "x"` dual-check — after phase 1 the legacy side is always `None`. Reduces ~10 lines of noise and eliminates the "which side wins" class of bugs.
-9. fp8 preflight (item 16): require `env_capabilities.compute_capability ≥ sm_90`, `torch_version ≥ 2.11`, and `torchao_version ≥ 0.17`. Warn if `use_cache` isn't explicitly `False`.
-
-### Phase 3 — Migrate remaining consumers
-
-10. `lm_eval/cli.py:120` → `flash_attention=cfg.attn_uses_flash_lib`.
-11. `lm_eval/__init__.py:26` currently reads `(cfg.attn_implementation == "flash")` — after canonicalization `"flash"` is never stored, so this evaluates `False` for every backend. Change to `cfg.attn_uses_flash_lib`.
-12. `validation.py:1137-1142` (NPU check) currently iterates `["flash_attention", "sdp_attention", "s2_attention"]` as string keys. Replace with `cfg.attn_implementation in {"flash_attention_2", "flash_attention_3", "sdpa", "s2"}` or the equivalent canonical-string set.
-13. `tests/e2e/multigpu/test_llama.py:524-526` → `cfg.attn_implementation = "flash_attention_2"` / `"flex_attention"`.
-14. **Xformers decision** (item 10): the old `cfg.flash_attention = True` side-effect activated `_patch_attention` for btlm/stablelm_epoch+packing/mistral3/llava. Two choices:
-    - Add `xformers` to the set that gates `_patch_attention` (restore old behaviour, keeps patches live).
-    - Document that those patches don't apply to xformers post-refactor and drop the paths if they're dead.
-    Pick one explicitly and leave a commit note. Do not leave it as silent breakage.
-15. Add a repo-level check (`tests/test_no_legacy_attn_reads.py` or a ruff/grep pre-commit) that fails if anything outside `config.py`'s normalizer reads `cfg.flash_attention` / `cfg.sdp_attention` / etc. Keeps the invariant from rotting.
-
-### Phase 4 — Tests + docs
-
-14. Rewrite `test_attn_implementation.py` to build full `AxolotlInputConfig(**data)`, not just the classmethod. Covers validator ordering and the Pydantic-field-override issue.
-15. Add one test per gap closed above: `attn_implementation: eager + sample_packing`; `attn_implementation: flex_attention + activation_offloading`; short-form `flash` rejected; `flash_attention_2` passthrough; `kernels-community/flash-attn3` capability lookup; `attn_uses_flash_lib: true` in YAML rejected; legacy boolean emits `DeprecationWarning` and is absent from validated `cfg`; fp8 preflight failures.
-16. Update `docs/attention.qmd` for the single `attn_implementation` field + the deprecation table for legacy flags. One-paragraph migration note in the changelog.
-17. `examples/` contains ~170 YAML files using legacy flags (`flash_attention: true` etc.). They still validate post-refactor (normalizer maps them with deprecation), but a follow-up sweep to convert them to `attn_implementation: flash_attention_2` is worth scheduling — call this out in the migration note so users know examples will be migrated on a later pass.
-
-## 5. Ordering & risk
-
-- Phase 1 is the keystone: it's the largest diff but each step is mechanical once the alias map is in place. No behaviour change for any consumer that was using `attn_implementation` correctly; behaviour change only for consumers that were reading legacy flags (phase 3 step 13 is the explicit decision point).
-- Phase 2 is independent of phase 1 and can land first as a quick safety net.
-- Phase 3 step 13 is the only judgment call — flag for review before choosing.
-- Total: ~10-13 commits beyond what's on the branch, each scoped and individually revertable.

VERSION

@@ -1 +1 @@
-0.16.0.dev0
+0.16.2.dev0

docker/Dockerfile

@@ -24,9 +24,9 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
 RUN pip uninstall -y causal_conv1d
 RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

docker/Dockerfile-base

@@ -58,19 +58,3 @@ RUN git lfs install --skip-repo && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
-
-# Map Python version (e.g., 3.12 -> cp312)
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
-    # Map architecture
-    case "$TARGETARCH" in \
-        amd64) ARCH_TAG="x86_64" ;; \
-        arm64) ARCH_TAG="aarch64" ;; \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
-    esac && \
-    WHL_VERSION="v0.7.16" && \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
-    pip3 install --no-cache-dir "${WHL_FILE}" && \
-    rm "${WHL_FILE}"

docker/Dockerfile-tests

@@ -24,9 +24,9 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docker/Dockerfile-uv

@@ -24,9 +24,9 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
 RUN uv pip uninstall causal_conv1d
 RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         uv pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

docker/Dockerfile-uv-base

@@ -38,20 +38,3 @@ RUN uv pip install packaging setuptools wheel psutil \
 RUN if [ "$TARGETARCH" = "amd64" ]; then \
         MAMBA_SKIP_CUDA_BUILD=TRUE CAUSAL_CONV1D_SKIP_CUDA_BUILD=TRUE uv pip install --no-build-isolation mamba_ssm causal_conv1d; \
     fi
-
-# Map Python version (e.g., 3.12 -> cp312)
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
-    LINUX_TAG="manylinux_" && \
-    # Map architecture
-    case "$TARGETARCH" in \
-        amd64) ARCH_TAG="2_24_x86_64.manylinux_2_28_x86_64" ;; \
-        arm64) ARCH_TAG="2_34_aarch64" ;; \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
-    esac && \
-    WHL_VERSION="v0.7.16" && \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-${LINUX_TAG}${ARCH_TAG}.whl" && \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
-    uv pip install --no-cache-dir "${WHL_FILE}" && \
-    rm "${WHL_FILE}"

docs/agents/new_model_support.md

@@ -121,11 +121,11 @@ Older models that use `_prepare_4d_causal_attention_mask` (Llama, Mistral, Qwen2
 
 | Backend | Config | head_dim limit | torch_compile | Notes |
 |---------|--------|---------------|---------------|-------|
-| FA2 | `attn_implementation: flash_attention_2` | 256 | ✅ | Fastest when supported |
-| FA4 | auto with `attn_implementation: flash_attention_2` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
-| SDPA | `attn_implementation: sdpa` | None | ✅ | Universal fallback |
-| flex | `attn_implementation: flex_attention` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
-| eager | `attn_implementation: eager` | None | ✅ | Slowest, always works |
+| FA2 | `flash_attention: true` | 256 | ✅ | Fastest when supported |
+| FA4 | auto with `flash_attention: true` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
+| SDPA | `sdp_attention: true` | None | ✅ | Universal fallback |
+| flex | `flex_attention: true` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
+| eager | neither set | None | ✅ | Slowest, always works |
 
 **Check model support**: Look at `_supports_flash_attn_2`, `_supports_flex_attn`, `_supports_sdpa` attributes on the model class.
 

docs/agents/sft.md

@@ -83,7 +83,7 @@ Watch for: loss never decreasing (check `train_on_inputs`, dataset, LR), loss go
 | Issue | Fix |
 |-------|-----|
 | OOM during training | Reduce `micro_batch_size`, enable `gradient_checkpointing`, reduce `sequence_len` |
-| `sample_packing` + SDPA + bf16 = 0.0 loss | Use `attn_implementation: flash_attention_2` or disable `sample_packing` |
+| `sample_packing` + SDPA + bf16 = 0.0 loss | Use `flash_attention: true` or disable `sample_packing` |
 | Missing chat template error | Set `chat_template: chatml` explicitly |
 | Label masking wrong | Run `axolotl preprocess config.yaml --debug` and inspect labels |
 | Loss NaN | Use `bf16: auto`, lower LR, check data for empty samples |

docs/attention.qmd

@@ -3,71 +3,28 @@ title: Attention
 description: Supported attention modules in Axolotl
 ---
 
-Axolotl routes attention via a single config field:
+## SDP Attention
+
+This is the default built-in attention in PyTorch.
 
 ```yaml
-attn_implementation: <backend>
+sdp_attention: true
 ```
 
-`attn_implementation` is passed through to `transformers` verbatim (via
-`model.config._attn_implementation`). Accepted values are the HF-native
-backends, axolotl-registered backends, or a hub-kernel path.
+For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
 
-## Backends
+## Flash Attention
 
-| `attn_implementation` | Description |
-|---|---|
-| `eager` | Plain PyTorch attention. No packing support. |
-| `sdpa` | PyTorch `scaled_dot_product_attention`. No packing support. |
-| `flash_attention_2` | Dao-AILab Flash Attention 2. |
-| `flash_attention_3` | Dao-AILab Flash Attention 3 (Hopper+). |
-| `flex_attention` | Torch Flex Attention (requires torch ≥ 2.6). |
-| `xformers` | xFormers memory-efficient attention. |
-| `sage` | SageAttention (QK int8 / PV fp16). |
-| `s2` | Shifted-Sparse Attention (LLaMA only, FA2 under the hood). |
-| `fp8` | torchao FP8 low-precision attention (requires SM90+, torch ≥ 2.11). Loaded as SDPA and patched post-load. |
-| `kernels-community/flash-attn3` | HF hub FA3 kernel. |
-| `kernels-community/sage-attention` | HF hub SageAttention kernel. |
-| Other `<org>/<name>` path | Any hub-kernel path supported by `transformers`. |
-
-Short-form aliases (`flash`, `fa2`, `flex`, `sdp`, etc.) are **not accepted** —
-set the canonical name above.
-
-### Capability flags
-
-Axolotl derives three boolean capability flags from `attn_implementation` and
-exposes them on the validated config:
-
-- `cfg.attn_supports_packing` — backend supports varlen sample packing via
-  `position_ids`. Gates multipack patches and `sample_packing_drop_attention_mask`.
-- `cfg.attn_uses_flash_lib` — backend needs the `flash_attn` (Dao-AILab)
-  monkeypatches (FA4 auto, LLaMA flash hijack, ring-FA).
-- `cfg.attn_needs_dtype_cast` — backend requires fp16/bf16 embeddings
-  (everything except `eager` and `sdpa`).
-
-These are **computed** — they cannot be overridden from YAML.
-
-## Per-backend notes
-
-### SDPA
-
-Default PyTorch attention. See
-[PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).
+Axolotl supports Flash Attention 2, 3, and 4. The best available version is used automatically
+based on your installed packages and GPU.
 
 ```yaml
-attn_implementation: sdpa
+flash_attention: true
 ```
 
-### Flash Attention
+For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
 
-Axolotl supports FA2, FA3, and FA4. The best available version is used
-automatically based on your installed packages and GPU.
-
-```yaml
-attn_implementation: flash_attention_2  # or flash_attention_3
-```
-
-#### Flash Attention 2
+### Flash Attention 2
 
 Requirements: Ampere, Ada, or Hopper GPUs (Turing or lower not supported)
 
@@ -82,20 +39,20 @@ Alternatively, try reinstall or downgrade a version.
 
 :::
 
-#### Flash Attention 3
+### Flash Attention 3
 
 Requirements: Hopper only and CUDA 12.8 (recommended)
 
 ```bash
 git clone https://github.com/Dao-AILab/flash-attention.git
 cd flash-attention/hopper
+
 python setup.py install
 ```
 
-#### Flash Attention 4
+### Flash Attention 4
 
-Requirements: Hopper or Blackwell GPUs. Auto-applied when `attn_uses_flash_lib`
-is true and FA4 is importable.
+Requirements: Hopper or Blackwell GPUs
 
 ```bash
 pip install flash-attn-4
@@ -106,6 +63,7 @@ Or from source:
 ```bash
 git clone https://github.com/Dao-AILab/flash-attention.git
 cd flash-attention/flash_attn/cute
+
 pip install -e .
 
 # FA2's flash_attn package includes a cute/ stub that shadows FA4.
@@ -128,113 +86,93 @@ and falls back to FA2/3.
 
 :::
 
+For more details: [flash-attention/flash_attn/cute](https://github.com/Dao-AILab/flash-attention/tree/main/flash_attn/cute)
+
 ### AMD
 
-Requirements: ROCm 6.0 and above. See
-[Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
+Requirements: ROCm 6.0 and above.
 
-### Flex Attention
+See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
+
+## Flex Attention
+
+A flexible PyTorch API for attention used in combination with `torch.compile`.
 
 ```yaml
-attn_implementation: flex_attention
-torch_compile: true  # recommended
+flex_attention: true
+
+# recommended
+torch_compile: true
 ```
 
-Requires torch ≥ 2.6. See [PyTorch docs](https://pytorch.org/blog/flexattention/).
+::: {.callout-note}
 
-### SageAttention
+We recommend using latest stable version of PyTorch for best performance.
 
-Requirements: Ampere, Ada, or Hopper GPUs.
+:::
+
+For more details: [PyTorch docs](https://pytorch.org/blog/flexattention/)
+
+## SageAttention
+
+Attention kernels with QK Int8 and PV FP16 accumulator.
 
 ```yaml
-attn_implementation: sage
+sage_attention: true
 ```
 
+Requirements: Ampere, Ada, or Hopper GPUs
+
 ```bash
 pip install sageattention==2.2.0 --no-build-isolation
 ```
 
 ::: {.callout-warning}
 
-Only LoRA/QLoRA recommended. Full finetuning has been observed to drop loss to 0. See
-[GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
+Only LoRA/QLoRA recommended at the moment. We found loss drop to 0 for full finetuning. See [GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
 
 :::
 
-For more details: [Sage Attention](https://github.com/thu-ml/SageAttention).
+For more details: [Sage Attention](https://github.com/thu-ml/SageAttention)
 
-### xFormers
+::: {.callout-note}
+
+We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
+
+:::
+
+
+## xFormers
 
 ```yaml
-attn_implementation: xformers
+xformers_attention: true
 ```
 
 ::: {.callout-tip}
 
-Recommended for Turing GPUs or below (e.g. Colab T4).
+We recommend using with Turing GPUs or below (such as on Colab).
 
 :::
 
-### Shifted Sparse Attention
+For more details: [xFormers](https://github.com/facebookresearch/xformers)
+
+## Shifted Sparse Attention
 
 ::: {.callout-warning}
 
-Planned for deprecation. Prefer one of the backends above.
+We plan to deprecate this! If you use this feature, we recommend switching to methods above.
 
 :::
 
-Requirements: LLaMA model architecture. Loaded as FA2 under the hood and
-patched to implement shifted-sparse attention. Does not support sample packing.
+Requirements: LLaMA model architecture
 
 ```yaml
-attn_implementation: s2
+flash_attention: true
+s2_attention: true
 ```
 
-### FP8
+::: {.callout-tip}
 
-torchao low-precision attention. Loaded as SDPA and patched post-load.
-
-Requirements: SM90+ (Hopper/Blackwell), PyTorch ≥ 2.11, torchao ≥ 0.17,
-flash-attn with FA3. KV caching must be disabled.
-
-```yaml
-attn_implementation: fp8
-```
-
-### Hub kernels
-
-```yaml
-attn_implementation: kernels-community/flash-attn3
-```
-
-Passed through to `transformers`; axolotl does not install the kernel itself.
-For recognized hub paths the capability flags are set automatically; for
-arbitrary paths axolotl uses conservative defaults (`attn_supports_packing=False`,
-`attn_uses_flash_lib=False`).
-
-## Migrating from legacy boolean flags
-
-The following legacy config fields are **deprecated** and will be removed in a
-future release. Each emits a `DeprecationWarning` when set and is stripped from
-the validated config.
-
-| Legacy | Canonical |
-|---|---|
-| `flash_attention: true` | `attn_implementation: flash_attention_2` |
-| `sdp_attention: true` | `attn_implementation: sdpa` |
-| `xformers_attention: true` | `attn_implementation: xformers` |
-| `flex_attention: true` | `attn_implementation: flex_attention` |
-| `sage_attention: true` | `attn_implementation: sage` |
-| `s2_attention: true` | `attn_implementation: s2` |
-| `eager_attention: true` | `attn_implementation: eager` |
-
-Combining `attn_implementation` with a legacy flag (e.g. `attn_implementation:
-flash_attention_2` **and** `flash_attention: true`) raises — pick one.
-
-::: {.callout-note}
-
-Existing example configs under `examples/` still use the legacy flags. They
-continue to work with a deprecation warning; they will be migrated in a
-follow-up pass.
+No sample packing support!
 
 :::

docs/debugging.qmd

@@ -77,8 +77,9 @@ Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/us
 
 ```bash
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv sync --extra flash-attn --extra deepspeed --group dev --group test
+uv venv --no-project --relocatable
 source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 #### Remote Hosts
@@ -218,8 +219,9 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 You will now be in the container.  Next, install Axolotl with dev dependencies:
 
 ```bash
-uv sync --extra flash-attn --extra deepspeed --group dev --group test
+uv venv --no-project --relocatable
 source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 ### Attach To Container

docs/docker.qmd

@@ -10,13 +10,16 @@ This section describes the different Docker images that are released by AxolotlA
 [Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with PyTorch 2.9.1 and CUDA 12.8.
-:::
+### Switch to the `-uv` images
 
-::: {.callout-tip}
-Each image below is available in a **uv variant** that uses [uv](https://docs.astral.sh/uv/) with
-a relocatable venv (`/workspace/axolotl-venv`) instead of Miniconda + pip. Append `-uv` to the image name
-(e.g. `axolotlai/axolotl-base-uv`). Tags follow the same format. We recommend the uv images for new deployments.
+Each image below ships a **uv variant** that uses [uv](https://docs.astral.sh/uv/) with a relocatable venv
+(`/workspace/axolotl-venv`) instead of Miniconda + pip. Append `-uv` to the image name
+(e.g. `axolotlai/axolotl-uv`, `axolotlai/axolotl-base-uv`, `axolotlai/axolotl-cloud-uv`). Tags follow the
+same format as their non-uv counterparts.
+
+**We recommend switching to the `-uv` images early.** In the near future we will publish the uv-based
+build to the non-uv tags as well. The non-uv names will continue to work, but they will start serving
+the uv image.
 :::
 
 ## Base
@@ -85,7 +88,7 @@ Tags examples:
 - `main-py3.12-cu130-2.10.0`
 - `main-latest`
 - `main-20260315-py3.11-cu128-2.9.1`
-- `0.12.0`
+- `0.16.1`
 
 ## Cloud
 

docs/ebft.qmd

@@ -129,7 +129,7 @@ gradient_accumulation_steps: 4
 max_steps: 20
 learning_rate: 5.0e-6
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 output_dir: ./outputs/ebft-quickstart
 ```
@@ -304,7 +304,7 @@ lora_alpha: 32
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flex_attention
+flex_attention: true
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true          # Required with flex_attention

docs/faq.qmd

@@ -57,7 +57,7 @@ description: Frequently asked questions
 
 **Q: vLLM is not working with Axolotl**
 
-> A: We currently recommend torch 2.6.0 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.11-cu124-2.6.0` tag.
+> A: We currently recommend torch 2.10 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.12-cu128-2.10.0` tag (note: torch 2.10 images are built with Python 3.12).
 
 **Q: FA2 2.8.0 `undefined symbol` runtime error on CUDA 12.4**
 

docs/grpo.qmd

@@ -154,7 +154,7 @@ lr_scheduler: cosine
 warmup_steps: 10
 
 bf16: true
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

docs/installation.qmd

@@ -15,7 +15,7 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 
 - NVIDIA GPU (Ampere architecture or newer for `bf16` and Flash Attention) or AMD GPU
 - Python ≥3.11
-- PyTorch ≥2.9.0
+- PyTorch ≥2.9.1
 
 ## Installation {#sec-installation}
 
@@ -36,9 +36,9 @@ source $HOME/.local/bin/env
 Choose your CUDA version (e.g. `cu128`, `cu130`), create a venv, and install:
 ```{.bash}
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv --no-project --relocatable
+uv venv
 source .venv/bin/activate
-uv pip install --no-build-isolation axolotl[flash-attn,deepspeed]
+uv pip install --no-build-isolation axolotl[deepspeed]
 ```
 
 ### Edge/Development Build {#sec-edge-build}
@@ -49,12 +49,11 @@ For the latest features between releases:
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv sync --extra flash-attn --extra deepspeed
+uv venv
 source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]'
 ```
 
-`uv sync` creates a `.venv`, installs exact pinned versions from `uv.lock`, and sets up an editable install automatically.
-
 ### Docker {#sec-docker}
 
 ```{.bash}
@@ -132,11 +131,11 @@ source $HOME/.local/bin/env
 
 # Create a fresh venv (recommended for a clean start)
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv --no-project --relocatable
+uv venv
 source .venv/bin/activate
 
 # Reinstall axolotl
-uv pip install --no-build-isolation axolotl[flash-attn,deepspeed]
+uv pip install --no-build-isolation axolotl[deepspeed]
 ```
 
 ## Using pip (Alternative) {#sec-pip}
@@ -151,13 +150,13 @@ Follow the instructions at: [https://pytorch.org/get-started/locally/](https://p
 
 ```{.bash}
 pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
+pip3 install --no-build-isolation axolotl[deepspeed]
 ```
 
 For editable/development installs:
 ```{.bash}
 pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install --no-build-isolation -e '.[deepspeed]'
 ```
 
 ## Troubleshooting {#sec-troubleshooting}

docs/optimizations.qmd

@@ -22,12 +22,12 @@ Improves GPU utilization by combining multiple short sequences into a single pac
 
 Using an optimized attention implementation is critical for training speed.
 
-- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `attn_implementation: flash_attention_2`. **(Recommended)** The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check [AMD Support](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support).
-- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `attn_implementation: flex_attention`.
-- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `attn_implementation: sdpa`. PyTorch's native implementation.
-- **[Xformers](https://github.com/facebookresearch/xformers)**: `attn_implementation: xformers`. Works with FP16.
+- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `flash_attention: true`. **(Recommended)** The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check [AMD Support](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support).
+- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `flex_attention: true`.
+- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `sdp_attention: true`. PyTorch's native implementation.
+- **[Xformers](https://github.com/facebookresearch/xformers)**: `xformers_attention: true`. Works with FP16.
 
-See [Attention](attention.qmd) for the full list of backends and the canonical values.
+*Note: You should only enable one attention backend.*
 
 ### LoRA Optimizations
 

docs/rlhf.qmd

@@ -1147,7 +1147,8 @@ datasets:
     type: ebft_strided_structured.transform
     split: train[:1%]
 
-attn_implementation: flex_attention   # Strided mode uses flex_attention
+flash_attention: false
+flex_attention: true     # Strided mode uses flex_attention
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true    # Required for flex_attention

docs/sequence_parallelism.qmd

@@ -55,7 +55,7 @@ To use sequence parallelism, you need:
 
 ## Limitations
 
-- Flash attention must be enabled for this to work (`attn_implementation: flash_attention_2` in config YAML)
+- Flash attention must be enabled for this to work (`flash_attention: true` in config YAML)
 - May have a small performance overhead due to communication between GPUs
 
 ## Example

docs/training_stability.qmd

@@ -245,7 +245,7 @@ For GRPO, also reduce `max_completion_length`. Memory scales quadratically with
 Reduces attention memory from O(n^2) to O(n):
 
 ```yaml
-attn_implementation: flash_attention_2
+flash_attention: true
 ```
 
 ### Step 6: Offload with DeepSpeed

examples/LiquidAI/README.md

@@ -15,7 +15,7 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
     Here is an example of how to install from pip:
     ```bash
     # Ensure you have a compatible version of Pytorch installed
-    uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    uv pip install --no-build-isolation 'axolotl>=0.16.1'
     ```
 
 2.  Run one of the finetuning examples below.

examples/LiquidAI/lfm2-350m-fft.yaml

@@ -39,7 +39,7 @@ tf32: true
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/LiquidAI/lfm2-8b-a1b-lora.yaml

@@ -48,7 +48,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -50,7 +50,8 @@ tf32: true
 
 gradient_checkpointing: true
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
+eager_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/alst/llama3-8b-deepspeed-alst.yaml

@@ -39,7 +39,7 @@ activation_offloading: legacy
 
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_steps: 100
 saves_per_epoch: 1

examples/alst/llama3-8b-fsdp2-alst.yaml

@@ -39,7 +39,7 @@ activation_offloading: legacy
 
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_steps: 100
 saves_per_epoch: 1

examples/apertus/README.md

@@ -11,11 +11,11 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
     Here is an example of how to install from main for pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-uv pip install --no-build-isolation -e '.[flash-attn]'
+uv pip install --no-build-isolation -e '.'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh

examples/apertus/apertus-8b-qlora.yaml

@@ -55,7 +55,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/arcee/README.md

@@ -13,11 +13,11 @@ Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the A
     Here is an example of how to install from main for pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-uv pip install --no-build-isolation -e '.[flash-attn]'
+uv pip install --no-build-isolation -e '.'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh

examples/arcee/afm-4.5b-qlora.yaml

@@ -55,7 +55,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/archived/cerebras/btlm-ft.yml

@@ -59,7 +59,8 @@ gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
 
-attn_implementation: flash_attention_2
+flash_attention: true
+sdp_attention:
 flash_optimum:
 
 gptq_groupsize:

examples/archived/cerebras/qlora.yml

@@ -39,7 +39,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/code-llama/13b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/13b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/34b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/34b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/7b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/7b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/dbrx/16bit-lora.yaml

@@ -52,7 +52,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/dbrx/8bit-lora.yaml

@@ -55,7 +55,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/dbrx/fft-ds-zero3.yaml

@@ -39,7 +39,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/deepcoder/deepcoder-14B-preview-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/archived/falcon/config-7b-lora.yml

@@ -43,7 +43,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/falcon/config-7b-qlora.yml

@@ -73,7 +73,8 @@ early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/falcon/config-7b.yml

@@ -40,7 +40,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/gemma/qlora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/gptj/qlora.yml

@@ -36,7 +36,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/jeopardy-bot/config.yml

@@ -37,7 +37,8 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/mpt-7b/config.yml

@@ -39,6 +39,7 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/config.yml

@@ -39,7 +39,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/lora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/qlora.yml

@@ -40,7 +40,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/qwen/lora.yml

@@ -47,6 +47,7 @@ tf32: false
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
+flash_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qlora.yml

@@ -47,6 +47,7 @@ tf32: false
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
+flash_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qwen2-moe-lora.yaml

@@ -43,7 +43,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qwen2-moe-qlora.yaml

@@ -46,7 +46,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/redpajama/config-3b.yml

@@ -40,6 +40,7 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/replit-3b/config-lora.yml

@@ -38,6 +38,7 @@ tf32: true
 gradient_checkpointing:
 resume_from_checkpoint:
 logging_steps: 1
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/stablelm-2/1.6b/fft.yml

@@ -44,7 +44,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 flash_attn_fuse_mlp: true

examples/archived/stablelm-2/1.6b/lora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 

examples/archived/starcoder2/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/tiny-llama/lora-mps.yml

@@ -47,6 +47,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
+flash_attention: false
 
 warmup_ratio: 0.1
 evals_per_epoch: 0

examples/archived/tiny-llama/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/tiny-llama/pretrain.yml

@@ -36,7 +36,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/tiny-llama/qlora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/xgen-7b/xgen-7b-8k-qlora.yml

@@ -71,7 +71,8 @@ early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/yi-34B-chat/qlora.yml

@@ -10,7 +10,7 @@ load_in_4bit: true
 sequence_len: 1024
 bf16: auto
 tf32: false
-attn_implementation: flash_attention_2
+flash_attention: true
 special_tokens:
   bos_token: "<|startoftext|>"
   eos_token: "<|endoftext|>"

examples/cohere/command-r-7b-qlora.yml

@@ -48,7 +48,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -36,12 +36,7 @@
     "id": "msOCO4NRmRLa"
    },
    "outputs": [],
-   "source": [
-    "%%capture\n",
-    "# This step can take ~5-10 minutes to install dependencies\n",
-    "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fec1a88\""
-   ]
+   "source": "%%capture\n# This step can take ~5-10 minutes to install dependencies\n!pip install --no-build-isolation \"axolotl>=0.16.1\"\n!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fec1a88\""
   },
   {
    "cell_type": "markdown",

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -35,7 +35,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -59,7 +59,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/devstral/README.md

@@ -15,8 +15,8 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
     Here is an example of how to install from pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
+uv pip install --no-build-isolation 'axolotl>=0.16.1'
 ```
 
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage

examples/devstral/devstral-small-qlora.yml

@@ -51,7 +51,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 scaling_softmax: true
 
 loss_watchdog_threshold: 5.0

examples/distributed-parallel/llama-3_1-8b-hsdp-tp.yaml

@@ -29,7 +29,7 @@ output_dir: ./outputs/ndp-out/
 
 sequence_len: 2048
 sample_packing: true
-attn_implementation: flash_attention_2
+flash_attention: true
 
 gradient_accumulation_steps: 1
 micro_batch_size: 1

examples/distributed-parallel/qwen3-8b-fsdp-tp-cp.yaml

@@ -26,7 +26,7 @@ output_dir: ./outputs/ndp-out/
 
 sequence_len: 8192
 sample_packing: true
-attn_implementation: flash_attention_2
+flash_attention: true
 
 gradient_accumulation_steps: 1
 micro_batch_size: 1  # must be 1 when using context parallel

examples/eaft/eaft-example.yml

@@ -65,7 +65,8 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
-attn_implementation: flash_attention_2
+xformers_attention:
+flash_attention: true
 
 warmup_ratio: 0.1
 weight_decay: 0.0

examples/ebft/llama-1b-ebft-opencode-novllm.yaml

@@ -46,7 +46,7 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-1b-ebft-opencode.yaml

@@ -66,7 +66,7 @@ lora_target_linear: true
 
 # --- Hardware ---
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-1b-ebft-strided-structured.yaml

@@ -47,7 +47,8 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flex_attention
+flash_attention: false  # strided EBFT overrides to flex_attention (or eager fallback) at runtime
+flex_attention: true    # fused flex_attention kernel compiles itself; don't set torch_compile: true
                         # (full-model compile conflicts with gradient checkpointing + flex_attention)
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:

examples/ebft/llama-1b-ebft-strided.yaml

@@ -46,6 +46,7 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
+flash_attention: false  # strided EBFT overrides to flex_attention (or eager fallback) at runtime
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-3b-ebft-strided-fft.yaml

@@ -48,6 +48,7 @@ lora_target_linear: true
 
 bf16: auto
 torch_dtype: bfloat16
+flash_attention: false
 gradient_checkpointing: true
 torch_compile: true
 gradient_checkpointing_kwargs:

examples/ebft/llama-8b-ebft-strided-fft.yaml

@@ -41,6 +41,7 @@ warmup_steps: 10
 weight_decay: 0.01
 
 bf16: auto
+flash_attention: false  # strided EBFT uses flex_attention at runtime
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false

examples/ebft/qwen35-4b-ebft-structured-async.yaml

@@ -72,7 +72,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/qwen35-4b-ebft-structured.yaml

@@ -63,7 +63,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/qwen35-9b-ebft-structured.yaml

@@ -68,7 +68,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/falcon-h1/falcon-h1-1b-deep-qlora.yaml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/falcon-h1/falcon-h1-1b-qlora.yaml

@@ -61,7 +61,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/falcon-h1/falcon-h1-34b-qlora.yaml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/falcon-h1/falcon-h1-3b-qlora.yaml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/falcon-h1/falcon-h1-500m-qlora.yaml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/falcon-h1/falcon-h1-7b-qlora.yaml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/gemma2/qlora.yml

@@ -53,7 +53,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/gemma2/reward-model.yaml

@@ -43,7 +43,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/gemma3/gemma-3-1b-qlora.yml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/gemma3/gemma-3-270m-qlora.yml

@@ -62,7 +62,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: