epoch

README.md

@@ -1,218 +1,201 @@
 ---
-license: apache-2.0
-language:
-- en
-metrics:
-- accuracy
-tags:
-- document
-- code
-- code2doc
-- instruction_tuned
-- basemodel
-- pytorch
-- docstring
-- python
-- documentation
-- text-generation-inference
 library_name: transformers
-pipeline_tag: text-generation
-widget:
-- text: "<code>def get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix):print(np_array_A_matrix)np_array_A_matrix += (np.full((int_num_states, int_num_states), float_eps) - (np.identity(int_num_states) * float_eps))print(np_array_A_matrix)np_array_D_matrix = np.diag(np.sum(np_array_A_matrix, axis=1))print(np_array_D_matrix)np_array_D_matrix_inv = np.linalg.inv(np_array_D_matrix)print(np_array_D_matrix_inv)np_array_P_matrix = np.dot(np_array_D_matrix_inv, np_array_A_matrix)print(np_array_P_matrix)print(np.sum(np_array_P_matrix, axis=1))return np_array_P_matrix</code><question>Document the python code above.</question><doc>"
-  example_title: "example"
-
+tags: []
 ---
-# pip-code-to-doc
 
-[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- 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. -->
+
+[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 -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### 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. -->
 
-[colab_notebook](https://colab.research.google.com/drive/17PyMU_3QN9LROy7x-jmaema0cuLRzBvc?usp=sharing)
+[More Information Needed]
 
-## What have we built?
-A 1.3 bn code documentation model that outperforms most models on documenting codes and making your in-house libs ready for LLM and RAG pipelines.
-We have also open sourced a [parsing lib](https://github.com/PipableAI/pip-library-parser) for the same, together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
-This is a further trained version of pip-sql-1.3b.
+## More Information [optional]
 
+[More Information Needed]
 
+## Model Card Authors [optional]
 
-## How we built it?
+[More Information Needed]
 
-We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
-Loss behaviour in the set up mentioned above - 
+## Model Card Contact
 
+[More Information Needed]
 
-## License
-The model is open source under apache 2.0. License
 
-## Usage
-
-
-### Library use
-```python
-!pip3 install git+https://github.com/PipableAI/pip-library-parser
-!pip3 install atlassian-python-api
-
-
-from pip_library_parser import CodeToDocGenerator
-from atlassian import Jira
-
-import torch
-torch.set_default_device("cuda")
-
-# Instantiate the CodeToDocGenerator
-generator = CodeToDocGenerator()
-
-# Generate docstrings for the module's functions and methods
-module = Jira
-module_name = "atlassian.Jira"
-
-docs = generator.generate_module_docs(module, module_name)
-print(docs)
-```
-
-```python
-from pip_library_parser import CodeToDocGenerator
-
-# Instantiate the CodeToDocGenerator
-generator = CodeToDocGenerator()
-
-code_snippet = """
-def example_function(x):
-    return x * 2
-"""
-
-docstring = generator.generate_docstring_from_pip_model(code_snippet)
-print("Generated Docstring:")
-print(docstring)
-```
-
-### Installation
-
-```bash
-pip install transformers
-```
-
-### Prompt
-```python
-prompt = f"""<function_code>{code}</function_code>
-<question>Give one line description of the python code above in natural language.</question>
-<doc>"""
-```
-
-### PyTorch
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-device = "cuda"
-model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-to-doc-1.3b").to(device)
-tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-to-doc-1.3b")
-prompt = f"""
-<function_code>
-def example_function(x):
-    return x * 2
-</function_code>
-<question>Give one line description of the python code above in natural language.</question>
-<doc>"""
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=300)
-tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split('</doc>')[0]
-```
-
-
-
-## Examples
-
-### prompt
-```python
-<function_code>
-###########################
-# Generate Analytical Model
-###########################
-##################################################
-# func: get_np_array_transition_probability_matrix
-##################################################
-def get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix):
-    print('np_array_A_matrix:')
-    print(np_array_A_matrix)
-    #####################################################
-    # Perturb the adjacency matrix to avoid singularities
-    #####################################################
-    np_array_A_matrix += (np.full((int_num_states, int_num_states), float_eps) - (np.identity(int_num_states) * float_eps))
-    print('np_array_A_matrix:')
-    print(np_array_A_matrix)
-    print('np_array_D_matrix:')
-    np_array_D_matrix = np.diag(np.sum(np_array_A_matrix, axis=1))
-    print(np_array_D_matrix)
-    print('np_array_D_matrix_inv:')
-    np_array_D_matrix_inv = np.linalg.inv(np_array_D_matrix)
-    print(np_array_D_matrix_inv)
-    print('\n\n')
-    print('np_array_P_matrix:')
-    np_array_P_matrix = np.dot(np_array_D_matrix_inv, np_array_A_matrix)
-    print(np_array_P_matrix)
-    print('np.sum(np_array_P_matrix, axis=1):')
-    print(np.sum(np_array_P_matrix, axis=1))
-    print('\n\n')
-    return np_array_P_matrix
-##################################################
-# func: get_np_array_perron_frobenius_eigen_vector
-##################################################
-def get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix):
-    np_array_perron_frobenius_matrix = np.linalg.matrix_power(np_array_P_matrix,1000)
-    np_array_perron_frobenius_vector = np_array_perron_frobenius_matrix[0,:]
-    print('np_array_perron_frobenius_matrix:')
-    print(np_array_perron_frobenius_matrix)
-    print('np.sum(np_array_perron_frobenius_matrix, axis=1):')
-    print(np.sum(np_array_perron_frobenius_matrix, axis=1))
-    print('np.sum(np_array_perron_frobenius_matrix, axis=0):')
-    print(np.sum(np_array_perron_frobenius_matrix, axis=0))
-    print('np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states:')
-    print(np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states)
-    print('np.dot(np_array_perron_frobenius_vector, np_array_P_matrix):')
-    print(np.dot(np_array_perron_frobenius_vector, np_array_P_matrix))
-    print('np_array_perron_frobenius_vector:')
-    print(np_array_perron_frobenius_vector)
-    print('\n\n')
-    return np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix
-#############################
-# func: get_np_array_Z_matrix
-#############################
-def get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix):
-    np_array_Z_matrix = np.linalg.inv(np.identity(int_num_states) - np_array_P_matrix + np_array_perron_frobenius_matrix)
-    print('np_array_Z_matrix:')
-    print(np_array_Z_matrix)
-    print('\n\n')
-    return(np_array_Z_matrix)
-#############################
-# func: get_np_array_H_matrix
-#############################
-def get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector):
-    np_array_H_matrix = np.zeros([int_num_states, int_num_states])
-    for i in range(int_num_states):
-        for j in range(int_num_states):
-            np_array_H_matrix[i][j] = (np_array_Z_matrix[j][j] - np_array_Z_matrix[i][j])/np_array_perron_frobenius_vector[j]
-    print('np_array_H_matrix:')
-    print(np_array_H_matrix)
-    print('\n\n')
-    return np_array_H_matrix
-###########
-# func: run
-###########
-def run(np_array_A_matrix):
-    int_num_states = len(np_array_A_matrix)
-    np_array_P_matrix = get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix)
-    np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix = get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix)
-    np_array_Z_matrix = get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix)
-    np_array_H_matrix = get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector)
-    return(np_array_H_matrix)
-</function_code>
-<question>Give one line description of the python code above in natural language.</question>
-<doc>
-```
-
-### Response
-```txt
-The given python code is a function that calculates the transition probability matrix, P, for a given adjacency matrix A, and then uses these matrices to calculate the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix which is the inverse of the Z matrix. The H matrix is then returned as the output of the function. The adjacency matrix A is a square matrix where each element at position (i, j) represents the probability of transitioning from state i to state j. The function first perturbs the adjacency matrix to avoid singularities, then calculates the transition probability matrix P, the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix. The H matrix is then returned as the output of the function.
-```
-
-### Team
-Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya 

model-00001-of-00002.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:697fbfcae2740a7b7964feae15c0bc5f699de0e80c962549840d49aef0b682e0
+oid sha256:00a3309db09b06ad2df7eb879d3b6ef3df635a8a4591f62a03621972682f95ef
 size 4986380064

model-00002-of-00002.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7db6ad5468af3edbd27d05a5d8e4ec405c33c4997a2767932c34ee73f8f64f6e
+oid sha256:c3bde8d98d16a8d6e5a9bdbe1a94e00f848265b419bf04a98d34f7a8fc0d11b0
 size 399532808

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ead7b223fe28322d216004963087f39100988fe08b3b2290cfa18b09290bf2c1
+oid sha256:e4398df56a5a218c969e711605e433d374526a114d1d563bccbe118c94984511
 size 5385980246