SS-Talk-2-Bash/README.md

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language:

• en license: apache-2.0 library_name: peft pipeline_tag: text-generation base_model: LiquidAI/LFM2.5-350M tags:
• bash
• terminal
• devops
• linux
• hardcoded
• lfm datasets:
• emirkaanozdemr/bash_command_data_6K model_creator: saadxsalman widget:
• text: "[NL] find all files larger than 100MB in the current directory [CL]" example_title: "Find Large Files"
• text: "[NL] list all files in long format including hidden ones [CL]" example_title: "List All Files" inference: parameters: do_sample: false temperature: 0.0 max_new_tokens: 64

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SS-Talk-2-Bash (LFM-350M-Hardcoded)

This model is a fine-tuned version of LiquidAI/LFM2.5-350M designed specifically for deterministic natural language to Bash command translation. It uses a Strict Hard-Coding training method to minimize linguistic "chatter" and maximize structural accuracy.

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1. Model Description

• Developed by: saadxsalman
• Model type: Causal Language Model (LFM)
• Language(s): English (Input) to Bash (Output)
• License: Apache 2.0
• Finetuned from model: LiquidAI/LFM2.5-350M

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2. Training Strategy: "The Hard-Coding Engine"

Unlike standard instruction-tuned models that learn to be "helpful assistants," this model was trained using a Masking Collator strategy:

• Label Masking: All natural language tokens (the prompt) are masked during training (loss = -100). The model only calculates loss on the Bash command itself.
• Zero Chatter: The model does not learn to say "Sure, here is your command." It is trained to jump directly from the [CL] token to the syntax.
• Greedy Decoding: The generation_config.json is locked to do_sample: False and temperature: 0.0 to ensure the same input always produces the same output.

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3. Training Data

The model was fine-tuned on the emirkaanozdemr/bash_command_data_6K dataset. The data was restructured into a rigid non-linguistic format: [NL] {Natural Language Prompt} [CL] {Bash Command} [END]

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4. Intended Use & Prompting

To get the best results, you must use the specific trigger tokens used during training.

Correct Prompt Format: [NL] find all files larger than 100MB in the current directory [CL]

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5. How to Use (Inference)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "saadxsalman/SS-Talk-2-Bash"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "[NL] list all files in long format [CL]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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6. Limitations and Biases

• Logic Only: This model has "forgotten" how to converse. It will not answer general questions or write Python code.
• Bash Specific: It is optimized for standard Linux Bash commands. It may struggle with complex shell scripting logic if not represented in the 6K training samples.
• Formatting Sensitive: If the [NL] or [CL] tokens are omitted, the model performance will degrade significantly.

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7. Training Hyperparameters

Parameter	Value
Learning Rate	1 \times 10^{-4}
Optimizer	Paged AdamW 8-bit
LoRA R	64
LoRA Alpha	128
Batch Size	16 (4 per device \times 4 grad accum)
Precision	Mixed Precision (FP16)