Image-Text-to-Text
Transformers
Safetensors
English
locateanything
feature-extraction
nvidia
eagle
vision
object-detection
grounding
conversational
custom_code
Instructions to use txchmechanicus/LocateAnything-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use txchmechanicus/LocateAnything-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="txchmechanicus/LocateAnything-3B", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("txchmechanicus/LocateAnything-3B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use txchmechanicus/LocateAnything-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "txchmechanicus/LocateAnything-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "txchmechanicus/LocateAnything-3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/txchmechanicus/LocateAnything-3B
- SGLang
How to use txchmechanicus/LocateAnything-3B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "txchmechanicus/LocateAnything-3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "txchmechanicus/LocateAnything-3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "txchmechanicus/LocateAnything-3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "txchmechanicus/LocateAnything-3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use txchmechanicus/LocateAnything-3B with Docker Model Runner:
docker model run hf.co/txchmechanicus/LocateAnything-3B
| # LA Flash Utils | |
| This folder contains the sparse attention utilities used by | |
| `LA_FLASH_ATTN=la_flash`. The release path is implemented with | |
| FlashAttention varlen over LocateAnything range plans. It does not include or | |
| build a local C++/CUDA extension. | |
| ## Features | |
| - Supports batched LocateAnything hybrid MTP inference on A100, RTX 4090, and H100. | |
| - Consumes Magi-style `q_ranges`, `k_ranges`, `segment_offsets`, and | |
| `attn_type_map` plans generated by `batch_utils.hybrid_runtime`. | |
| - Uses FlashAttention varlen for packed causal/full plans. | |
| - Packs LocateAnything MTP full-window key segments before calling | |
| FlashAttention, avoiding dense `[B,H,Q,K]` masks. | |
| - Supports log-sum-exp merging for compatible non-packed multi-segment plans. | |
| ## Attention Types | |
| The release path intentionally supports only FlashAttention-compatible plan | |
| types: | |
| | Value | Meaning | | |
| | --- | --- | | |
| | `0` | Full attention over the listed key segment or packed key segments. | | |
| | `1` | Bottom-right causal attention. | | |
| ## How It Works | |
| `batch_utils.hybrid_runtime` builds sparse range plans for the text decoder. | |
| Each plan describes which query token intervals attend to which key/value token | |
| intervals. `kernel_utils.range_attention` executes those plans with | |
| FlashAttention instead of materializing dense SDPA masks. | |
| The runtime follows three paths: | |
| - **Packed simple plans:** when each query range maps to one contiguous | |
| key/value range, LA Flash flattens the selected ranges, builds FlashAttention | |
| `cu_seqlens_q` / `cu_seqlens_k`, and calls `flash_attn_varlen_func` directly. | |
| - **Packed MTP full-window plans:** for hybrid MTP decode, multiple full | |
| key/value windows for the same query block are concatenated into one packed | |
| key/value sequence before the FlashAttention call. This keeps the sparse | |
| memory profile without constructing a `[B,H,Q,K]` attention mask. | |
| - **Compatible multi-segment plans:** when a query range attends to multiple | |
| segments that cannot be packed as one sequence, each segment is evaluated with | |
| FlashAttention and the partial outputs are merged with the standard | |
| log-sum-exp softmax composition. | |
| The output tensor shape and dtype match the decoder attention output expected | |
| by the model. This path is inference-oriented and depends on FlashAttention's | |
| forward kernels; it is not a custom autograd training backend. | |
| ## Runtime Knobs | |
| | Variable | Default | Meaning | | |
| | --- | --- | --- | | |
| | `LA_FLASH_ATTN` | `sdpa` | Set to `la_flash` to enable this backend through `batch_utils`. | | |
| | `LA_FLASH_FASTPATH` | `auto` | Use FlashAttention varlen for packed simple plans. | | |
| | `LA_FLASH_SEGMENT_FASTPATH` | `auto` | Use FlashAttention varlen for multi-segment sparse plans. Full segments are packed first; other compatible segments use LSE merging. | | |
| | `LA_FLASH_PLAN_STATS` | `0` | Record sparse plan statistics in inference summaries. | | |
| ## Notes | |
| Dense prefill and stock worker-style generation should keep | |
| `LA_FLASH_DENSE_BACKEND=sdpa`; LA Flash is used for sparse range plans | |
| produced by `batch_utils`. | |
| This package is for inference and evaluation. Training remains on the | |
| MagiAttention backend; the batched sparse-plan decode runtime does not support | |
| the `labels` training path. | |
| ## Source Layout | |
| - `range_attention.py`: FlashAttention varlen dispatch, sparse KV packing, LSE | |
| merge fallback, and availability checks. | |