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README.md

@@ -1,207 +1,99 @@
 ---
 base_model: allenai/OLMo-1B-hf
 library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:allenai/OLMo-1B-hf
-- lora
-- transformers
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# OLMo Code Python2-3 Tagged Model
 
+This is a LoRA adapter fine-tuned on the OLMo-1B model for Python 2 and 3 code generation tasks with language tagging.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
+- **Base Model:** allenai/OLMo-1B-hf
+- **Model Type:** LoRA Adapter
+- **Task:** Causal Language Modeling for Python 2 and 3 code
+- **Language:** Python 2 and 3 with language tagging
+- **License:** MIT
+- **Fine-tuned by:** dipikakhullar
 
-- **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 Description
 
-### Model Sources [optional]
+This model is a LoRA adapter that has been fine-tuned on Python 2 and 3 code data with language tagging. It extends the capabilities of the base OLMo-1B model specifically for Python code generation tasks, with the ability to distinguish between Python 2 and Python 3 syntax.
 
-<!-- Provide the basic links for the model. -->
+### LoRA Configuration
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **LoRA Type:** LORA
+- **LoRA Alpha:** 16
+- **LoRA Dropout:** 0.05
+- **LoRA Rank (r):** 8
+- **Target Modules:** down_proj, q_proj, v_proj, up_proj, k_proj, gate_proj, o_proj
+- **Task Type:** CAUSAL_LM
 
 ## 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]
+This model is intended for Python 2 and 3 code generation tasks with language tagging. It can be used to:
+- Generate Python code completions for both Python 2 and 3
+- Assist with code writing in both Python versions
+- Provide code suggestions with language awareness
+- Handle Python 2 to Python 3 migration tasks
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be further fine-tuned for specific Python programming tasks or integrated into code generation applications that need to handle both Python versions.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is specifically designed for Python 2 and 3 code generation and may not perform well for:
+- Other programming languages
+- Natural language tasks
+- Non-code related tasks
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+# Load the base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")
 
-## How to Get Started with the Model
+# Load the LoRA adapter
+model = PeftModel.from_pretrained(base_model, "dipikakhullar/olmo-code-python2-3-tagged")
 
-Use the code below to get started with the model.
+# Example usage for Python 3
+prompt = "[python3] def fibonacci(n):"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
-[More Information Needed]
+# Example usage for Python 2
+prompt = "[python2] def fibonacci(n):"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## 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]
+The model was fine-tuned on cleaned Python 2 and 3 code data with language tagging, specifically prepared for language model training.
 
 ### 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]
+- **Base Model:** allenai/OLMo-1B-hf
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Checkpoint:** checkpoint-2100
 
 ## Model Card Contact
 
-[More Information Needed]
-### Framework versions
+- **Author:** dipikakhullar
+- **Repository:** https://huggingface.co/dipikakhullar/olmo-code-python2-3-tagged
+
+## Framework versions
 
-- PEFT 0.16.0
+- PEFT 0.7.1
+- Transformers