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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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

Precision: fp16 mixed precision

Epochs: 3

Batch Size: 2 (gradient accumulation = 2)

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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### 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]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.15.2