| --- |
| 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. |
