File size: 2,305 Bytes
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license: mit
base_model: hongyuw/ft-bitvla-bitsiglipL-224px-libero_object-bf16
tags:
- gguf
- robotics
- bitvla
---
# BitVLA LIBERO-Object I2S for Robot.cpp
Four GGUF components for Robot.cpp's BitVLA runtime. The source is the author's
[BitVLA checkpoint](https://huggingface.co/hongyuw/ft-bitvla-bitsiglipL-224px-libero_object-bf16/tree/7fd7a4f2cdb968a4d05759ff6ffb37667abc84f6),
revision `7fd7a4f2cdb968a4d05759ff6ffb37667abc84f6`. This is the fine-tuned version without robotics pre-training.
The normalization key is `libero_object_no_noops`.
## Files and precision
- `bitvla-llm-i2s.gguf`
- `bitvla-vision-i2s.gguf`
- `bitvla-proprio-i2s.gguf`
- `bitvla-action-i2s.gguf`
The converter applies BitVLA WeightQuant in BF16, then packs 366 ternary matrices
as `i2s-matrix-v1`. Other tensors retain BF16/F32 storage. I2S is a Robot.cpp-specific
layout; these files require its patched gguf/ggml runtime, not a standalone llama.cpp CLI.
Use all four components from the same checkpoint.
## Run
Use [Robot.cpp with BitVLA support](https://github.com/Robot-cpp/robot.cpp/tree/cf0712c2751fdd8519de22f822b7baa1b08b5937) and apply its llama.cpp patches before building. Use a model-server binary built from this revision or a later compatible revision.
```bash
GGUF_DIR=/path/to/bitvla-libero-object-i2s
/path/to/model-server \
--model-type bitvla \
--llm "${GGUF_DIR}/bitvla-llm-i2s.gguf" \
--mmproj "${GGUF_DIR}/bitvla-vision-i2s.gguf" \
--state-proj "${GGUF_DIR}/bitvla-proprio-i2s.gguf" \
--action-expert "${GGUF_DIR}/bitvla-action-i2s.gguf" \
--threads 8 --host 127.0.0.1 --port 5555
```
For these LIBERO checkpoints, send two 224×224 HWC RGB uint8 images in the `full_image`, then `wrist_image` order,
eight raw proprio values, and a nonempty task instruction. Resize/crop images
according to the source checkpoint's evaluation preprocessing before sending them.
The runtime normalizes images/state and returns an 8×7 denormalized action chunk.
Robot-specific action postprocessing remains the client's responsibility.
## Validation
Release checks verify all four GGUF headers and SHA256 hashes. Uploaded file sizes
and SHA256 hashes are compared with the local conversion outputs. This model card
does not claim a new success-rate, numerical-equivalence, or latency result.
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