Upload 7 files

README.md

@@ -1,137 +1,202 @@
 ---
-license: apache-2.0
-base_model:
-- mistralai/Mistral-7B-Instruct-v0.3
+base_model: mistralai/Mistral-7B-v0.1
+library_name: peft
 ---
-# Introduction
 
-## Task Description
-This model was fine-tuned to improve its ability to perform spatial
-reasoning tasks. The objective is to enable the model to interpret natural language queries related to
-spatial relationships, directions, and locations and output actionable responses.
-The task addresses limitations in current LLMs, which often fail to perform precise spatial reasoning,
-such as determining relationships between points on a map, planning routes, or identifying locations
-based on bounding boxes. 
+# Model Card for Model ID
 
-## Task Importance
-Spatial reasoning is important for a wide range of applications such as navigation and geospatial
-analysis. Many smaller LLMs, while strong in general reasoning, often lack the ability to interpret
-spatial relationships with precision or utilize real-world geographic data effectively. For example, they
-struggle to answer queries like “What’s between Point A and Point B?” or “Find me the fastest route
-avoiding traffic at 8 AM tomorrow.” I came across this limitation through my work, in which I am
-working on prompt engineering for an LLM project that has agentic behavior in calling a geocoding
-API. Even when the LLM has access to geospatial information, smaller models struggled to correctly
-interpret user questions, so we had to switch to a much newer and larger model. 
+<!-- Provide a quick summary of what the model is/does. -->
 
-## Related Work/Gap Analysis
 
-While there is ongoing research in integrating LLMs with geospatial systems, most existing solutions
-rely on symbolic AI or rule-based systems rather than leveraging the generalization capabilities of
-LLMs. Additionally, the paper “Advancing Spatial Reasoning in Large Language Models: An In-Depth
-Evaluation and Enhancement Using the StepGame Benchmark,” concluded that larger models like
-GPT-4 perform well in mapping natural language descriptions to spatial relations but struggle with
-multi-hop reasoning. This paper used the StepGame as a benchmark for spatial reasoning.
-Fine-tuning a model fills the gap identified in the paper, as the only solutions identified in their
-research was prompt engineering with Chain of Thought.
 
-Research by organizations like OpenAI and Google has focused on improving contextual reasoning
-through fine-tuning, but there is limited work targeting spatial reasoning.
+## Model Details
 
-## Main Results
+### Model Description
 
+<!-- Provide a longer summary of what this model is. -->
 
 
-# Training Data
 
-For this fine-tuning task the step-game dataset was used. This dataset is large and provides multi-step
-reasoning challenges for geospatial reasoning. The train-test split is predefined with 50,000 rows in the train split and 10,000 in the test
-split. It focuses on multi-step problem-solving with
-spatial relationships, such as directional logic, relative positioning, and route-based reasoning. It
-presents text-based tasks that require stepwise deductions, ensuring the model develops strong
-reasoning abilities beyond simple fact recall. This dataset follows the template of story,
-question and answer to assess spatial reasoning as depicted below.
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-![Description of StepGame Training Data](https://huggingface.co/sareena/spatial_lora_mistral/resolve/main/stepgame.jpg)
+### Model Sources [optional]
 
+<!-- Provide the basic links for the model. -->
 
-# Training Method
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-For this task of spatial reasoning LoRA (Low-Rank Adaptation)  was used as the training method. 
-LoRA allows for efficient fine-tuning of large language models by freezing the majority of the model weights and only updating small, 
-low-rank adapter matrices within attention layers. It significantly reduces the computational cost and memory requirements of full 
-fine-tuning, making it ideal for working with limited GPU resources. LoRA is especially effective for task-specific
-adaptation when the dataset is moderately sized and instruction formatting is consistent as in the case of this dataset of stepGame. 
-In previous experiments with spatial reasoning fine-tuning, LoRA performed better than prompt tuning. While prompt tuning resulted in close to 0% accuracy on both the StepGame and
-MMLU evaluations, LoRA preserved partial task performance (18% accuracy) and retained some general knowledge ability (46% accuracy on 
-MMLU geography vs. 52% before training). I used a learning rate of 2e-4, batch size of 8, and trained for 2 epochs. 
-This setup preserved general reasoning ability while improving spatial accuracy.
+## Uses
 
-# Evaluation
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
+### Direct Use
 
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-# Usage and Intended Uses
-This model is designed to assist with natural language spatial reasoning, particularly in tasks that involve multi-step relational 
-inference between objects or locations described in text. This could be implemented in agentic spatial systems and/or text-based game bots.
+[More Information Needed]
 
+### Downstream Use [optional]
 
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+[More Information Needed]
 
-model = AutoModelForCausalLM.from_pretrained("sareena/spatial_lora_mistral")
-tokenizer = AutoTokenizer.from_pretrained("sareena/spatial_lora_mistral")
+### Out-of-Scope Use
 
-inputs = tokenizer("Q: The couch is to the left of the table. The lamp is on the couch. Where is the lamp?", return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-```
+[More Information Needed]
 
-# Prompt Format
-The model is trained on instruction-style input with a spatial reasoning question:
+## Bias, Risks, and Limitations
 
-```text
-Q: The couch is to the left of the table. The lamp is on the couch. Where is the lamp in relation to the table?
-```
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-# Expected Output Format
-The output is a short, natural language spatial answer:
+[More Information Needed]
 
-```text
-A: left 
-```
+### Recommendations
 
-# Limitations 
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-The model is still limited in deep reasoning capabilities and sometimes fails multi-hop spatial tasks. 
-LoRA helps balance this trade-off, but fine-tuning on more diverse spatial tasks could yield stronger generalization. 
-)erformance on the SpatialEval benchmark dropped drastically, due to incompatibility between the prompt style used for training and the
-multiple-choice formatting in SpatialEval. Future work to remediate this would be to test more prompt formats in training or use instruction-tuned datasets
-more similar to the downstream evaluations.m 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## Citation 
+## How to Get Started with the Model
 
-1. **Hendrycks, Dan**, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt.  
-   *"Measuring Massive Multitask Language Understanding."* arXiv preprint arXiv:2009.03300 (2020).
+Use the code below to get started with the model.
 
-2. **Li, Fangjun**, et al.  
-   *“Advancing Spatial Reasoning in Large Language Models: An in-Depth Evaluation and Enhancement Using the StepGame Benchmark.”*  
-   *arXiv.Org*, 8 Jan. 2024. [https://arxiv.org/abs/2401.03991](https://arxiv.org/abs/2401.03991)
+[More Information Needed]
 
-3. **Mirzaee, Roshanak**, Hossein Rajaby Faghihi, Qiang Ning, and Parisa Kordjamshidi.  
-   *"SpartQA: A Textual Question Answering Benchmark for Spatial Reasoning."* arXiv preprint arXiv:2104.05832 (2021).
+## Training Details
 
-4. **Shi, Zhengxiang**, et al.  
-   *“StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts.”*  
-   *arXiv.Org*, 18 Apr. 2022. [https://arxiv.org/abs/2204.08292](https://arxiv.org/abs/2204.08292)
+### Training Data
 
-5. **Shi, Zhengxiang**, Qiang Zhang, and Aldo Lipani.  
-   *"StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts."* arXiv preprint arXiv:2204.08292 (2022).
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-6. **Wang, Mila**, Xiang Lorraine Li, and William Yang Wang.  
-   *"SpatialEval: A Benchmark for Spatial Reasoning Evaluation."* arXiv preprint arXiv:2104.08635 (2021).
+[More Information Needed]
 
-7. **Weston, Jason**, Antoine Bordes, Sumit Chopra, and Tomas Mikolov.  
-   *"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks."* arXiv preprint arXiv:1502.05698 (2015).
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.15.1

adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "mistralai/Mistral-7B-v0.1",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69bd02adcb3c97a720467e452ba2a2c83589ffe568acc39fd5fa5032fc05d83b
+size 13648432

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}