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README.md
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<!-- Provide the basic links for the model. -->
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- **Repository:**
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- **Paper [optional]:**
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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[More Information Needed]
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## Training Details
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Results
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## Model Examination [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://huggingface.co/prital27/tinyllama-lora-cli-utils
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- **Paper [optional]:** N/A
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- **Demo [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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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.
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### Downstream Use [optional]
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This adapter can be integrated into a CLI assistant application or chatbot for developers and system administrators.
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### Out-of-Scope Use
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- Not suitable for general conversation or non-technical queries.
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- Not intended for security-sensitive operations (e.g., altering SSH settings on production systems).
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- May produce incorrect or unsafe commands if misused.
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## Bias, Risks, and Limitations
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- Does not generalize well to non-trained or very obscure command-line tools.
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- May hallucinate incorrect or risky commands if given vague instructions.
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- No safety layer is applied to verify command validity.
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### Recommendations
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- Use with human supervision.
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- Always validate generated commands before execution.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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tokenizer = AutoTokenizer.from_pretrained("prital27/tinyllama-lora-cli-utils")
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base = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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model = PeftModel.from_pretrained(base, "prital27/tinyllama-lora-cli-utils")
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prompt = "### Question:\nHow do I search for TODOs recursively?\n\n### Answer:\n"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(outputs[0]))
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## Training Details
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#### Training Hyperparameters
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Precision: fp16 mixed precision
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Epochs: 3
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Batch Size: 2 (gradient accumulation = 2)
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Learning Rate: 2e-4
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Results
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Accuracy on direct prompts: ~85%
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Basic shell command correctness: high
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Limitations on multi-line/bash scripting: present
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#### Summary
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The model reliably suggests shell commands for common CLI tasks. Performance degrades on ambiguous prompts or complex multi-line scripts.
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## Model Examination [optional]
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