feat(coach): add local transformers provider

README.md

@@ -71,16 +71,19 @@ requires installing MMPose/MMCV and mapping model keypoints into the shared land
 
 ## Hugging Face ZeroGPU
 
-When this app runs on a Hugging Face Space with ZeroGPU hardware selected, only the compute-heavy
-pose backend and router inference calls are wrapped with `spaces.GPU`. The API and pipeline layer
-stay outside the ZeroGPU boundary so request state, streaming progress queues, and file serving are
-not serialized into the GPU worker.
+When this app runs on a Hugging Face Space with ZeroGPU hardware selected, compute-heavy model
+calls are wrapped with `spaces.GPU`. The API and pipeline layer stay outside the ZeroGPU boundary
+so request state, streaming progress queues, and file serving are not serialized into the GPU
+worker.
 
 The runtime uses these defaults:
 
 - `SPACES_ZERO_GPU=1`: set by Hugging Face ZeroGPU; enables CUDA for the Torch exercise router.
 - `POZIFY_ROUTER_DEVICE`: optional override for router inference, for example `cpu` or `cuda`.
 - `POZIFY_SPACES_GPU_DURATION`: optional `spaces.GPU` duration in seconds, default `120`.
+- `POZIFY_COACH_SUMMARY_PROVIDER`: `hf_inference` by default. Set `local_transformers`
+  to run the coach summary model inside the Space instead of calling Hugging Face Inference.
+- `POZIFY_COACH_SUMMARY_MODEL`: coach model id, default `Qwen/Qwen2.5-7B-Instruct`.
 
 The MediaPipe Tasks backend tries its GPU delegate on ZeroGPU and falls back to CPU if unavailable.
 The older `mp.solutions.pose` path remains CPU-only. The Torch exercise router loads and predicts
@@ -88,6 +91,18 @@ inside its ZeroGPU-wrapped function, so model objects are not pickled across the
 The root `packages.txt` installs the native GLES/EGL/OpenGL libraries required by MediaPipe Tasks
 on the Hugging Face runtime.
 
+For a fully local coach on ZeroGPU, set:
+
+```bash
+POZIFY_COACH_SUMMARY_PROVIDER=local_transformers
+POZIFY_COACH_SUMMARY_MODEL=Qwen/Qwen2.5-7B-Instruct
+POZIFY_SPACES_GPU_DURATION=300
+```
+
+The 7B model is viable on ZeroGPU `large` for short coach-summary prompts, but the first request can
+be slow while the model downloads and loads. Use a smaller Qwen2.5 Instruct model if queue time or
+cold-start time matters more than output quality.
+
 When running in real mode, the UI summary now shows:
 
 - `Analysis mode`: `mock` or `real`

pyproject.toml

@@ -5,6 +5,7 @@ description = "Gradio base app for Pozify with mocked pipeline steps and explici
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
+    "accelerate>=0.26.0",
     "fastapi>=0.136.3",
     "gradio>=4.44.0",
     "huggingface-hub>=0.24.0",
@@ -18,6 +19,7 @@ dependencies = [
     "scikit-learn>=1.4.0",
     "spaces>=0.40.0",
     "torch==2.11.0",
+    "transformers>=4.51.0",
 ]
 
 [project.optional-dependencies]

requirements.txt

@@ -2,6 +2,8 @@
 #    uv export --format requirements.txt --no-dev --no-hashes --frozen --output-file requirements.txt
 absl-py==2.4.0
     # via mediapipe
+accelerate==1.14.0
+    # via pozify
 aiohappyeyeballs==2.6.2
     # via aiohttp
 aiohttp==3.14.1
@@ -121,9 +123,12 @@ httpx==0.28.1
     #   spaces
 huggingface-hub==1.18.0
     # via
+    #   accelerate
     #   gradio
     #   gradio-client
     #   pozify
+    #   tokenizers
+    #   transformers
 hyperframe==6.1.0
     # via h2
 idna==3.18
@@ -171,6 +176,7 @@ networkx==3.6.1 ; python_full_version >= '3.11'
     # via torch
 numpy==1.26.4
     # via
+    #   accelerate
     #   contourpy
     #   gradio
     #   matplotlib
@@ -181,6 +187,7 @@ numpy==1.26.4
     #   pozify
     #   scikit-learn
     #   scipy
+    #   transformers
 nvidia-cublas==13.1.0.3 ; sys_platform == 'linux'
     # via
     #   cuda-toolkit
@@ -228,11 +235,13 @@ orjson==3.11.9
     # via gradio
 packaging==26.2
     # via
+    #   accelerate
     #   gradio
     #   gradio-client
     #   huggingface-hub
     #   matplotlib
     #   spaces
+    #   transformers
 pandas==2.3.3 ; python_full_version < '3.11' or python_full_version >= '3.14'
     # via gradio
 pandas==3.0.3 ; python_full_version >= '3.11' and python_full_version < '3.14'
@@ -247,6 +256,8 @@ propcache==0.5.2
     #   yarl
 protobuf==6.33.6
     # via modal
+psutil==7.2.2
+    # via accelerate
 pycparser==3.0 ; implementation_name != 'PyPy'
     # via cffi
 pydantic==2.12.5
@@ -275,8 +286,12 @@ pytz==2026.2
     #   pandas
 pyyaml==6.0.3
     # via
+    #   accelerate
     #   gradio
     #   huggingface-hub
+    #   transformers
+regex==2026.5.9
+    # via transformers
 requests==2.34.2
     # via spaces
 rich==15.0.0
@@ -285,6 +300,10 @@ rich==15.0.0
     #   typer
 safehttpx==0.1.7
     # via gradio
+safetensors==0.8.0
+    # via
+    #   accelerate
+    #   transformers
 scikit-learn==1.7.2 ; python_full_version < '3.11'
     # via pozify
 scikit-learn==1.9.0 ; python_full_version >= '3.11'
@@ -315,14 +334,22 @@ synchronicity==0.12.3
     # via modal
 threadpoolctl==3.6.0
     # via scikit-learn
+tokenizers==0.22.2
+    # via transformers
 toml==0.10.2
     # via modal
 tomlkit==0.14.0
     # via gradio
 torch==2.11.0
-    # via pozify
+    # via
+    #   accelerate
+    #   pozify
 tqdm==4.68.2
-    # via huggingface-hub
+    # via
+    #   huggingface-hub
+    #   transformers
+transformers==5.12.0
+    # via pozify
 triton==3.6.0 ; sys_platform == 'linux'
     # via torch
 typer==0.25.1
@@ -330,6 +357,7 @@ typer==0.25.1
     #   gradio
     #   hf-gradio
     #   huggingface-hub
+    #   transformers
 types-certifi==2021.10.8.3
     # via modal
 types-toml==0.10.8.20260518

src/pozify/slm/providers.py

@@ -2,9 +2,10 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 import os
-from typing import Protocol
+from typing import Any, Protocol
 
 from pozify.env import env_truthy, load_local_env
+from pozify.hf_spaces import default_spaces_gpu_duration, spaces_gpu
 
 
 PROVIDER_ENV = "POZIFY_COACH_SUMMARY_PROVIDER"
@@ -16,6 +17,10 @@ HF_TOKEN_ENV = "HF_TOKEN"
 
 DEFAULT_PROVIDER = "hf_inference"
 DEFAULT_MODEL = "Qwen/Qwen2.5-7B-Instruct"
+LOCAL_TRANSFORMERS_PROVIDER = "local_transformers"
+LOCAL_TRANSFORMERS_ALIASES = {LOCAL_TRANSFORMERS_PROVIDER, "local", "transformers"}
+
+_LOCAL_TRANSFORMERS_CACHE: dict[str, tuple[Any, Any]] = {}
 
 
 @dataclass(frozen=True)
@@ -125,19 +130,156 @@ class HFInferenceCoachSummaryModel:
         raise RuntimeError("No supported Hugging Face inference method is available")
 
 
+def _gpu_duration(*_args: object, **_kwargs: object) -> int:
+    return default_spaces_gpu_duration()
+
+
+def _load_local_transformers_backend(model: str, token: str | None) -> tuple[Any, Any]:
+    cache_key = f"{model}:{token or ''}"
+    cached = _LOCAL_TRANSFORMERS_CACHE.get(cache_key)
+    if cached is not None:
+        return cached
+
+    try:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+    except ImportError as exc:  # pragma: no cover - dependency declared in project
+        raise RuntimeError(
+            "transformers and torch are required for local coach summary inference"
+        ) from exc
+
+    tokenizer = AutoTokenizer.from_pretrained(model, token=token)
+    language_model = AutoModelForCausalLM.from_pretrained(
+        model,
+        device_map="auto",
+        torch_dtype="auto",
+        token=token,
+    )
+    language_model.eval()
+
+    cache_entry = (tokenizer, language_model)
+    _LOCAL_TRANSFORMERS_CACHE[cache_key] = cache_entry
+    return cache_entry
+
+
+def _local_transformers_device(language_model: Any) -> Any:
+    device = getattr(language_model, "device", None)
+    if device is not None:
+        return device
+
+    try:
+        return next(language_model.parameters()).device
+    except StopIteration as exc:
+        raise RuntimeError("Local coach summary model has no parameters") from exc
+
+
+@spaces_gpu(duration=_gpu_duration)
+def _generate_local_transformers_summary(
+    *,
+    model: str,
+    prompt: str,
+    max_tokens: int,
+    temperature: float,
+    token: str | None,
+) -> str:
+    try:
+        import torch
+    except ImportError as exc:  # pragma: no cover - dependency declared in project
+        raise RuntimeError("torch is required for local coach summary inference") from exc
+
+    tokenizer, language_model = _load_local_transformers_backend(model, token)
+    messages = [
+        {
+            "role": "system",
+            "content": "Return JSON only.",
+        },
+        {
+            "role": "user",
+            "content": prompt,
+        },
+    ]
+    if hasattr(tokenizer, "apply_chat_template"):
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+    else:
+        text = "\n\n".join(f"{message['role']}: {message['content']}" for message in messages)
+
+    inputs = tokenizer([text], return_tensors="pt")
+    model_inputs = inputs.to(_local_transformers_device(language_model))
+    generation_kwargs: dict[str, Any] = {
+        "max_new_tokens": max_tokens,
+        "do_sample": temperature > 0,
+    }
+    if temperature > 0:
+        generation_kwargs["temperature"] = temperature
+    if getattr(tokenizer, "eos_token_id", None) is not None:
+        generation_kwargs["pad_token_id"] = tokenizer.eos_token_id
+
+    with torch.inference_mode():
+        generated = language_model.generate(**model_inputs, **generation_kwargs)
+
+    prompt_length = model_inputs["input_ids"].shape[-1]
+    generated = generated[:, prompt_length:]
+    text = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
+    if not isinstance(text, str) or not text.strip():
+        raise RuntimeError("Local transformers inference returned empty output")
+    return text
+
+
+class LocalTransformersCoachSummaryModel:
+    def __init__(
+        self,
+        *,
+        model: str = DEFAULT_MODEL,
+        max_tokens: int = 700,
+        temperature: float = 0.1,
+        token: str | None = None,
+    ) -> None:
+        self.model = model
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.token = token
+
+    def generate_summary(self, prompt: str) -> CoachSummaryGeneration:
+        text = _generate_local_transformers_summary(
+            model=self.model,
+            prompt=prompt,
+            max_tokens=self.max_tokens,
+            temperature=self.temperature,
+            token=self.token,
+        )
+        return CoachSummaryGeneration(
+            text=text,
+            provider=LOCAL_TRANSFORMERS_PROVIDER,
+            model=self.model,
+        )
+
+
 def get_coach_summary_model() -> CoachSummaryModel | None:
     load_local_env()
 
-    if env_truthy(os.getenv(DISABLE_REMOTE_ENV)):
-        return None
-
     provider = os.getenv(PROVIDER_ENV, DEFAULT_PROVIDER).strip().lower()
-    if provider != "hf_inference":
+    if provider == "hf_inference":
+        if env_truthy(os.getenv(DISABLE_REMOTE_ENV)):
+            return None
+        return HFInferenceCoachSummaryModel(
+            model=os.getenv(MODEL_ENV, DEFAULT_MODEL),
+            max_tokens=_env_int(MAX_TOKENS_ENV, 700),
+            temperature=_env_float(TEMPERATURE_ENV, 0.1),
+            token=os.getenv(HF_TOKEN_ENV),
+        )
+
+    if provider in LOCAL_TRANSFORMERS_ALIASES:
+        return LocalTransformersCoachSummaryModel(
+            model=os.getenv(MODEL_ENV, DEFAULT_MODEL),
+            max_tokens=_env_int(MAX_TOKENS_ENV, 700),
+            temperature=_env_float(TEMPERATURE_ENV, 0.1),
+            token=os.getenv(HF_TOKEN_ENV),
+        )
+
+    if env_truthy(os.getenv(DISABLE_REMOTE_ENV)):
         return None
 
-    return HFInferenceCoachSummaryModel(
-        model=os.getenv(MODEL_ENV, DEFAULT_MODEL),
-        max_tokens=_env_int(MAX_TOKENS_ENV, 700),
-        temperature=_env_float(TEMPERATURE_ENV, 0.1),
-        token=os.getenv(HF_TOKEN_ENV),
-    )
+    return None

tests/test_coach_summary.py

@@ -23,6 +23,7 @@ from pozify.contracts import (
 )
 from pozify.env import load_local_env
 from pozify.knowledge_cards import retrieve_cards
+import pozify.slm.providers as slm_providers
 from pozify.steps import coach_summary, coach_summary_fallback, verifier
 
 
@@ -165,6 +166,42 @@ class CoachSummaryTests(unittest.TestCase):
                     "Qwen/Qwen2.5-7B-Instruct",
                 )
 
+    def test_get_coach_summary_model_can_use_local_transformers_provider(self) -> None:
+        local_payload = '{"summary":"ok"}'
+        with (
+            patch.dict(
+                os.environ,
+                {
+                    "POZIFY_COACH_SUMMARY_PROVIDER": "local_transformers",
+                    "POZIFY_COACH_SUMMARY_MODEL": "Qwen/Qwen2.5-7B-Instruct",
+                    "POZIFY_COACH_SUMMARY_MAX_TOKENS": "123",
+                    "POZIFY_COACH_SUMMARY_TEMPERATURE": "0",
+                },
+                clear=True,
+            ),
+            patch.object(slm_providers, "load_local_env"),
+            patch.object(
+                slm_providers,
+                "_generate_local_transformers_summary",
+                return_value=local_payload,
+            ) as generate,
+        ):
+            provider = slm_providers.get_coach_summary_model()
+
+            self.assertIsNotNone(provider)
+            generation = provider.generate_summary("coach prompt")
+
+        self.assertEqual(generation.provider, "local_transformers")
+        self.assertEqual(generation.model, "Qwen/Qwen2.5-7B-Instruct")
+        self.assertEqual(generation.text, local_payload)
+        generate.assert_called_once_with(
+            model="Qwen/Qwen2.5-7B-Instruct",
+            prompt="coach prompt",
+            max_tokens=123,
+            temperature=0.0,
+            token=None,
+        )
+
     def test_card_retrieval_is_deterministic_and_grounded(self) -> None:
         cards = retrieve_cards(
             profile=_profile(),

uv.lock

@@ -34,6 +34,24 @@ wheels = [
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 ]
 
+[[package]]
+name = "accelerate"
+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8d/75/94cd5d389649578aca399e5aa822637eec18319a1dadc400ffe2f9a7493f/accelerate-1.14.0.tar.gz", hash = "sha256:41b9c4377a54e0b460a959b0defa1b736e4ca0a2373252d9a539964c2afe3c8d", size = 412167, upload-time = "2026-06-11T13:45:52.326Z" }
+wheels = [
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+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.2"
@@ -2524,6 +2542,7 @@ name = "pozify"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "accelerate" },
     { name = "fastapi" },
     { name = "gradio" },
     { name = "huggingface-hub" },
@@ -2537,6 +2556,7 @@ dependencies = [
     { name = "scikit-learn", version = "1.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "spaces" },
     { name = "torch" },
+    { name = "transformers" },
 ]
 
 [package.optional-dependencies]
@@ -2549,6 +2569,7 @@ train = [
 
 [package.metadata]
 requires-dist = [
+    { name = "accelerate", specifier = ">=0.26.0" },
     { name = "datasets", marker = "extra == 'train'", specifier = ">=2.20.0" },
     { name = "fastapi", specifier = ">=0.136.3" },
     { name = "gradio", specifier = ">=4.44.0" },
@@ -2563,6 +2584,7 @@ requires-dist = [
     { name = "scikit-learn", specifier = ">=1.4.0" },
     { name = "spaces", specifier = ">=0.40.0" },
     { name = "torch", specifier = "==2.11.0" },
+    { name = "transformers", specifier = ">=4.51.0" },
 ]
 provides-extras = ["dev", "train"]
 
@@ -2709,6 +2731,34 @@ wheels = [
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 ]
 
+[[package]]
+name = "psutil"
+version = "7.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/c6/d1ddf4abb55e93cebc4f2ed8b5d6dbad109ecb8d63748dd2b20ab5e57ebe/psutil-7.2.2.tar.gz", hash = "sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372", size = 493740, upload-time = "2026-01-28T18:14:54.428Z" }
+wheels = [
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