thinkthink-dev
/

bashgemma-270m

@@ -1,207 +1,95 @@
 ---
 base_model: unsloth/functiongemma-270m-it
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/functiongemma-270m-it
-- lora
-- transformers
 ---
-# 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. -->
-- **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. -->
-[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 0.17.1

 ---
+license: apache-2.0
 base_model: unsloth/functiongemma-270m-it
 library_name: peft
 tags:
+  - bash
+  - code-generation
+  - function-calling
+  - nl2bash
+  - lora
+datasets:
+  - NL2Bash
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# BashGemma 270M
+Fine-tuned FunctionGemma 270M for natural language to bash command translation.
+**Paper**: [Zenodo DOI: 10.5281/zenodo.18058613](https://zenodo.org/records/18058613)
+## Model Description
+BashGemma translates natural language queries into structured JSON tool calls representing bash commands. It achieves **57.4% NLC2CMD accuracy** on NL2Bash test data—a 52.9 percentage point improvement over the base FunctionGemma model.
+| Metric | Baseline | BashGemma |
+|--------|----------|-----------|
+| NLC2CMD Accuracy | 0.045 | **0.574** |
+| Utility Match | 0.000 | **0.595** |
+| Parse Rate | 0.000 | **0.995** |
+## Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/functiongemma-270m-it",
+    torch_dtype=torch.float32,
+)
+model = PeftModel.from_pretrained(base_model, "thinkthink-dev/bashgemma-270m")
+tokenizer = AutoTokenizer.from_pretrained("thinkthink-dev/bashgemma-270m")
+# Generate
+prompt = "<start_of_turn>user\nFind all Python files in the current directory<end_of_turn>\n<start_of_turn>model\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=150, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=False))
+# {"name": "find", "arguments": {"name": "'*.py'", "path": "."}}
+```
+## Capabilities
+**Strong performance on:**
+- `find` with name, type, size, mtime filters
+- Simple pipelines (2-3 commands)
+- Common utilities: `grep`, `ls`, `cat`, `wc`, `sort`
+**Limitations:**
+- Complex multi-step operations
+- Utilities not in training data (`rsync`, `tar`, etc.)
+- Complex sed/awk patterns
+## Training
+- **Base Model**: unsloth/functiongemma-270m-it
+- **Method**: LoRA (rank=64, alpha=128)
+- **Training Data**: 9,153 NL2Bash examples
+- **Training Time**: 36 minutes on Apple M4 Max
+- **Approach**: Response-only training (mask instruction tokens)
+## Citation
+```bibtex
+@software{large2024bashgemma,
+  author = {Large, Jack},
+  title = {BashGemma: Fine-tuning a 270M Parameter Model for Natural Language to Bash Translation},
+  year = {2024},
+  publisher = {Zenodo},
+  doi = {10.5281/zenodo.18058613},
+  url = {https://zenodo.org/records/18058613}
+}
+```
+## License
+Apache 2.0 (same as base Gemma model)