updated

README.md

@@ -1,3 +1,73 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model: google/codegemma-2b
+tags:
+- nl2bash
+- text-to-code
+- code-generation
+- terminal
+- command-line
+pipeline_tag: text-generation
+---
+
+# zero-nl2cmds-v1: An AI Terminal Assistant Model
+
+This is a fine-tuned version of `google/codegemma-2b` designed to translate natural language instructions into precise Linux/macOS bash commands. It's the core component for an AI-powered command-line assistant.
+
+**Author:** Sanjayyy06
+**Version:** 1.0
+
+## Model Description
+
+This model takes a simple instruction, like "create a single directory named 'api'", and outputs the corresponding bash command, `mkdir api`. It has been specifically trained and corrected to handle common command-line tasks with a high degree of literal precision.
+
+## Intended Use
+
+This model is intended to be the inference engine for a local command-line interface (CLI) tool. A user can type a command in plain English, and the tool will use this model to generate and execute the shell command.
+
+```python
+# Example usage with the transformers library
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "Sanjayyy06/zero-nl2cmds-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Instruction: list all files in long format\nOutput:"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate the command
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Expected output: Instruction: list all files in long format
+#                  Output: ls -l
+
+
+
+
+#Training Process
+-The model was fine-tuned using a multi-stage process to ensure high accuracy and control over its behavior.
+-Initial Fine-Tuning: The base google/codegemma-2b model was first fine-tuned on a 5,000-example subset of the AnishJoshi/nl2bash-custom dataset.
+-Overfitting Correction: Early tests showed the model began to severely overfit on common patterns (e.g., generating entire project structures for a simple mkdir command).
+-Surgical Strike: A high-quality, 500-example "correctional dataset" was created to explicitly re-teach the model the literal meaning of simple commands.
+-Final Model: The model from step 1 was then fine-tuned for a short duration on the correctional dataset, resulting in this final version which is both knowledgeable and controllable.
+
+
+#Citation
+If you use this model in your work, please cite it as follows:
+
+Code snippet
+
+@misc{sanjayyy06_zero_nl2cmds_v1,
+  author = {Sanjayyy06},
+  title = {zero-nl2cmds-v1: A Surgically Corrected CodeGemma Model for NL-to-Bash Translation},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{[https://huggingface.co/Sanjayyy06/zero-nl2cmds-v1](https://huggingface.co/Sanjayyy06/zero-nl2cmds-v1)}},
+}
+
+
+