Update README.md
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README.md
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base_model: unsloth/Llama-3.2-1B-Instruct
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---
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###
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Natural language → Linux command. A compact Llama 3.2 1B Instruct model fine‑tuned (LoRA) to turn plain‑English requests into correct shell commands.
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@@ -40,7 +40,7 @@ ollama --version
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3) Run the model interactively:
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```bash
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ollama run
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```
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Then type a request, e.g.:
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- "List all files in the current directory with detailed information"
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4) One‑off (non‑interactive):
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```bash
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ollama run
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# Expected: head -n 5 access.log
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```
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5) Get command‑only answers (when needed):
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```bash
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ollama run
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# Expected: uname -a
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else None)
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```python
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from unsloth import FastLanguageModel
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model_id = "
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model, tokenizer = FastLanguageModel.from_pretrained(model_name=model_id, max_seq_length=2048)
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FastLanguageModel.for_inference(model)
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author = {Harshvardhan Vatsa},
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title = {Linux Command Generator (Llama 3.2 1B)},
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year = {2025},
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url = {https://huggingface.co/
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}
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```
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base_model: unsloth/Llama-3.2-1B-Instruct
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---
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### mecha-org/linux-command-generator-llama3.2-1b
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Natural language → Linux command. A compact Llama 3.2 1B Instruct model fine‑tuned (LoRA) to turn plain‑English requests into correct shell commands.
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3) Run the model interactively:
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```bash
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ollama run mecha-org/linux-command-generator-llama3.2-1b
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```
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Then type a request, e.g.:
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- "List all files in the current directory with detailed information"
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4) One‑off (non‑interactive):
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```bash
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ollama run mecha-org/linux-command-generator-llama3.2-1b -p "Display the first 5 lines of access.log"
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# Expected: head -n 5 access.log
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```
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5) Get command‑only answers (when needed):
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```bash
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ollama run mecha-org/linux-command-generator-llama3.2-1b -p "Output only the command with no explanation. Show system information including kernel version"
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# Expected: uname -a
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "mecha-org/linux-command-generator-llama3.2-1b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else None)
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```python
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from unsloth import FastLanguageModel
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model_id = "mecha-org/linux-command-generator-llama3.2-1b"
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model, tokenizer = FastLanguageModel.from_pretrained(model_name=model_id, max_seq_length=2048)
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FastLanguageModel.for_inference(model)
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author = {Harshvardhan Vatsa},
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title = {Linux Command Generator (Llama 3.2 1B)},
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year = {2025},
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url = {https://huggingface.co/mecha-org/linux-command-generator-llama3.2-1b}
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}
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```
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