strands-transformers 0.4.3

uv pip install -e .                # editable from source
uv pip install -e ".[audio]"       # soundfile, librosa  (mp3/flac/ogg decode)
uv pip install -e ".[vision]"      # torchvision (needed by VLMs!), opencv, av
uv pip install -e ".[training]"    # trl, peft, accelerate
uv pip install -e ".[all]"         # everything

WAV audio works with no extras. Vision models (SmolVLM, Qwen-VL, …) need [vision]. device="auto" picks cuda → mps → cpu (bf16 on GPU).

# 🖼️ image → "Green."   (local VLM brain, content blocks)
import io
from PIL import Image
from strands import Agent
from strands_transformers import TransformerModel

png = io.BytesIO(); Image.new("RGB", (224, 224), (20, 200, 40)).save(png, "PNG")
agent = Agent(model=TransformerModel(model_path="HuggingFaceTB/SmolVLM-256M-Instruct"))
print(agent([
    {"image": {"format": "png", "source": {"bytes": png.getvalue()}}},
    {"text": "What color is this image? One word."},
]))  # → Green.

# 🎬 video → "BRIGHTER."   (a video content block of brightening frames)
import asyncio, numpy as np
from PIL import Image
from strands_transformers import TransformerModel

model = TransformerModel(model_path="HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
                         params={"max_tokens": 48, "do_sample": False})
frames = [Image.fromarray(np.full((224, 224, 3), v, np.uint8)) for v in (10,40,80,120,160,200,230,250)]
msgs = [{"role": "user", "content": [
    {"video": {"format": "mp4", "fps": 2.0, "source": {"bytes": frames}}},
    {"text": "Does this video get brighter or darker? Answer brighter or darker."},
]}]
async def go():
    return "".join([e.get("contentBlockDelta",{}).get("delta",{}).get("text","")
                    async for e in model.stream(msgs)])
print(asyncio.run(go()))  # → ...brighter...

# 🧰 tool screenshot → "Blue."   (an image returned inside a toolResult)
import asyncio, io
from PIL import Image
from strands_transformers import TransformerModel

blue = io.BytesIO(); Image.new("RGB", (224, 224), (25, 25, 210)).save(blue, "PNG")
model = TransformerModel(model_path="HuggingFaceTB/SmolVLM-256M-Instruct",
                         params={"max_tokens": 32, "do_sample": False})
msgs = [
    {"role": "user", "content": [{"text": "Capture the screen, then name its color."}]},
    {"role": "assistant", "content": [{"toolUse": {"name": "capture", "toolUseId": "t1", "input": {}}}]},
    {"role": "user", "content": [{"toolResult": {"toolUseId": "t1", "status": "success", "content": [
        {"text": "Here is the captured screen:"},
        {"image": {"format": "png", "source": {"bytes": blue.getvalue()}}}]}}]},
    {"role": "user", "content": [{"text": "Dominant color of the captured screen? One word."}]},
]
async def go():
    return "".join([e.get("contentBlockDelta",{}).get("delta",{}).get("text","")
                    async for e in model.stream(msgs)])
print(asyncio.run(go()))  # → Blue.

# 📄 document → recovers "BANANA-42"   (a document content block → text LM prompt)
import asyncio
from strands_transformers import TransformerModel

model = TransformerModel(model_path="Qwen/Qwen3-0.6B", enable_thinking=False,
                         params={"max_tokens": 64, "do_sample": False})
body = b"The secret passphrase for the vault is BANANA-42. Keep it safe."
msgs = [{"role": "user", "content": [
    {"document": {"name": "secret", "format": "txt", "source": {"bytes": body}}},
    {"text": "What is the secret passphrase? Answer with just the passphrase."},
]}]
async def go():
    return "".join([e.get("contentBlockDelta",{}).get("delta",{}).get("text","")
                    async for e in model.stream(msgs)])
print(asyncio.run(go()))  # → BANANA-42

# 🔊 text → speech → text   (TTS then ASR, the tool path; the library narrating itself)
from strands_transformers import use_transformers

tts = use_transformers(action="run", task="text-to-audio",
                       model="facebook/mms-tts-eng",
                       inputs="the quick brown fox jumps over the lazy dog")
wav = tts["artifacts"][0]
asr = use_transformers(action="run", task="automatic-speech-recognition",
                       model="openai/whisper-tiny", inputs=wav)
print(asr["content"][0]["text"])  # → "...quick brown fox..."

# 🦾 camera + instruction → robot actions [1, 30, 6]   (VLA via the `call` path)
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download
from strands_transformers import use_transformers

REPO = "allenai/MolmoAct2-SO100_101"
top  = Image.open(hf_hub_download(REPO, "assets/sample_realsense_top_rgb.png")).convert("RGB")
side = Image.open(hf_hub_download(REPO, "assets/sample_realsense_side_rgb.png")).convert("RGB")
state = [-0.527, 189.14, 181.41, 60.64, -3.60, 1.097]

use_transformers(action="call", target="AutoProcessor.from_pretrained",
    parameters={"pretrained_model_name_or_path": REPO, "trust_remote_code": True}, cache_key="proc")
use_transformers(action="call", target="AutoModelForImageTextToText.from_pretrained",
    parameters={"pretrained_model_name_or_path": REPO, "trust_remote_code": True, "dtype": "float32"}, cache_key="vla")
print(use_transformers(action="call", target="cached:vla.predict_action",
    parameters={"processor": "cached:proc", "images": [top, side],
                "task": "Pick up the lemon and drop it in the red bowl.",
                "state": state, "norm_tag": "so100_so101_molmoact2",
                "inference_action_mode": "continuous", "num_steps": 10})["content"][0]["text"][:200])
# → MolmoAct2ActionOutput ... actions [1, 30, 6]

Omni audio-in + speech-out needs one bigger model - see examples/omni_audio.py.

pip install "strands-transformers[mcp]"
strands-transformers-mcp