---
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets:
- homebrewltd/Pick-Place-Table-Reasoning-local-pos-v0.2
library_name: transformers
license: apache-2.0
pipeline_tag: robotics
---

# AlphaSpace-1.5B

## Introduction

**"AlphaSpace:** ([Paper](https://huggingface.co/papers/2503.18769)), a novel methodology designed to enhance the spatial reasoning capabilities of language models for robotic manipulation in 3D Cartesian space. AlphaSpace employs a hierarchical semantics-based tokenization strategy that encodes spatial information at both coarse and fine-grained levels. Our approach represents objects with their attributes, positions, and height information through structured tokens, enabling precise spatial reasoning without relying on traditional vision-based embeddings. This approach enables LLMs to accurately manipulate objects by positioning them at specific [x, y, z] coordinates. 

Code: https://github.com/AlanDao/AlphaSpace

## Model Details
* Model architecture: [Deepseek-R1-Distil-Qwen-1.5B Instruct](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
* Dataset:
  * Training: [homebrewltd/Pick-Place-Table-Reasoning-local-pos-v0.2](https://huggingface.co/datasets/homebrewltd/Pick-Place-Table-Reasoning-local-pos-v0.2)
  * Eval: https://huggingface.co/datasets/EmbodiedBench/EB-Manipulation.
* License: Apache-2.0 license
* Developed by: Alan Dao, Dinh Bach Vu, Bui Quang Huy (Menlo Research)


## How to Get Started 

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
import torch
from utils import tokenize_desk, SYSTEM_PROMPT

# Load the mode


model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Define your workspace
objects = [
    {"red-cube": [51, 43, 17]},
    {"black-cube": [44, 58, 17]},
    {"purple-cube": [74, 59, 17]},
    {"green-cube": [65, 82, 17]},
]

# Give a natural language instruction
instruction = "Throw the red cube on top of the blue cylinder"
desk, object_height = tokenize_desk(objects)
final_instruction = SYSTEM_PROMPT.format(object_height=object_height,instruction=instruction,TABLE_MAP=desk)
chat = [
    {"role": "user", "content": final_instruction.strip()}
]
tokenized_chat = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, use_system_prompt=False, return_tensors="pt")
# print(len(tokenized_chat[0]))
generated_ids = model.generate(
    tokenized_chat.to("cuda"),
    max_new_tokens=2048,
    do_sample=False,
    temperature=0.6,
)
# Get the solution
result = tokenizer.decode(generated_ids[0][tokenized_chat.shape[1]:], skip_special_tokens=True)
print(result)
```
### Hardware

**GPU Configuration**: Cluster of 8x NVIDIA H200-SXM-140GB.

**GPU Usage**:
  - **SFT**: 40 mins.

### Training Arguments

We utilize [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) library to train the model. 

| **Parameter** | **Continual Training** |
| --- | --- |
| **Epoch** | 1 |
| **Global batch size** | 128 |
| **Learning Rate** | 1e-4 |
| **Learning Scheduler** | cosine with warmup |
| **Optimizer** | [AdamW Fused](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html) |
| **Warmup Ratio** | 0.1 |
| **Max length** | 4096 |
| **Precision** | bf16 |

##  Citation
- https://arxiv.org/abs/2503.18769

## More Information
* Contact the authors at alan@menlo.ai, bach@menlo.ai, yuuki@menlo.ai for further details.