104 lines
3.4 KiB
Markdown
104 lines
3.4 KiB
Markdown
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---
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language:
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- en
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license: apache-2.0
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library_name: peft
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pipeline_tag: text-generation
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base_model: LiquidAI/LFM2.5-350M
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tags:
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- bash
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- terminal
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- devops
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- linux
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- hardcoded
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- lfm
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datasets:
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- emirkaanozdemr/bash_command_data_6K
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model_creator: saadxsalman
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widget:
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- text: "[NL] find all files larger than 100MB in the current directory [CL]"
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example_title: "Find Large Files"
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- text: "[NL] list all files in long format including hidden ones [CL]"
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example_title: "List All Files"
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inference:
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parameters:
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do_sample: false
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temperature: 0.0
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max_new_tokens: 64
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---
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# SS-Talk-2-Bash (LFM-350M-Hardcoded)
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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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---
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## 1. Model Description
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* **Developed by:** saadxsalman
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* **Model type:** Causal Language Model (LFM)
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* **Language(s):** English (Input) to Bash (Output)
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* **License:** Apache 2.0
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* **Finetuned from model:** LiquidAI/LFM2.5-350M
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---
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## 2. Training Strategy: "The Hard-Coding Engine"
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Unlike standard instruction-tuned models that learn to be "helpful assistants," this model was trained using a **Masking Collator** strategy:
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* **Label Masking:** All natural language tokens (the prompt) are masked during training ($loss = -100$). The model only calculates loss on the Bash command itself.
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* **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.
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* **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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---
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## 3. Training Data
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The model was fine-tuned on the `emirkaanozdemr/bash_command_data_6K` dataset. The data was restructured into a rigid non-linguistic format:
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`[NL] {Natural Language Prompt} [CL] {Bash Command} [END]`
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---
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## 4. Intended Use & Prompting
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To get the best results, you **must** use the specific trigger tokens used during training.
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**Correct Prompt Format:**
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`[NL] find all files larger than 100MB in the current directory [CL]`
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---
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## 5. How to Use (Inference)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "saadxsalman/SS-Talk-2-Bash"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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prompt = "[NL] list all files in long format [CL]"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=64)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## 6. Limitations and Biases
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* **Logic Only:** This model has "forgotten" how to converse. It will not answer general questions or write Python code.
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* **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.
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* **Formatting Sensitive:** If the `[NL]` or `[CL]` tokens are omitted, the model performance will degrade significantly.
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---
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## 7. Training Hyperparameters
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| Parameter | Value |
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| :--- | :--- |
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| **Learning Rate** | $1 \times 10^{-4}$ |
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| **Optimizer** | Paged AdamW 8-bit |
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| **LoRA R** | 64 |
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| **LoRA Alpha** | 128 |
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| **Batch Size** | 16 (4 per device $\times$ 4 grad accum) |
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| **Precision** | Mixed Precision (FP16) |
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```
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