saadxsalman
/

SS-Talk-2-Bash

@@ -1,199 +1,103 @@
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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
 ---
+# 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.
+---
+## 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
+---
+## 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.
+---
+## 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]`
+---
+## 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]`
+---
+## 5. How to Use (Inference)
+```python
+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))
+```
+---
+## 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.
+---
+## 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) |
+```