---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
tags:
- lora
- cli
- command-line
- fine-tuned
- ssh
- grep
- git
- sed
- tar
---

# 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:** prital27
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** prital27
- **Model type:** causal_lm
- **Language(s) (NLP):** en
- **License:** apache-2.0
- **Finetuned from model [optional]:** TinyLlama/TinyLlama-1.1B-Chat-v1.0

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/prital27/tinyllama-lora-cli-utils
- **Paper [optional]:**  N/A
- **Demo [optional]:** [More Information Needed]

## Uses


### Direct Use

This model is fine-tuned for answering CLI-related questions. It is best suited for generating shell command suggestions for tasks involving tools like `git`,`tar`, `ssh`, general Unix commands and basic 'sed' and 'grep' commands. Ideal for use in AI assistants, terminal copilots, or educational tools.

### Downstream Use [optional]

This adapter can be integrated into a CLI assistant application or chatbot for developers and system administrators.


### Out-of-Scope Use

- Not suitable for general conversation or non-technical queries.
- Not intended for security-sensitive operations (e.g., altering SSH settings on production systems).
- May produce incorrect or unsafe commands if misused.

## Bias, Risks, and Limitations

- Does not generalize well to non-trained or very obscure command-line tools.
- May hallucinate incorrect or risky commands if given vague instructions.
- No safety layer is applied to verify command validity.

### Recommendations

- Use with human supervision.
- Always validate generated commands before execution.

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

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("prital27/tinyllama-lora-cli-utils")
base = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base, "prital27/tinyllama-lora-cli-utils")

prompt = "### Question:\nHow do I search for TODOs recursively?\n\n### Answer:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))


## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

Precision: fp16 mixed precision

Epochs: 3

Batch Size: 2 (gradient accumulation = 2)

Learning Rate: 2e-4
#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[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

Accuracy on direct prompts: ~85%

Basic shell command correctness: high

Limitations on multi-line/bash scripting: present

#### Summary

The model reliably suggests shell commands for common CLI tasks. Performance degrades on ambiguous prompts or complex multi-line scripts.

## 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

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### Framework versions

- PEFT 0.15.2