Update README.md

README.md

@@ -1,21 +1,184 @@
 ---
-base_model: unsloth/Llama-3.2-1B-Instruct
+title: Linux Command Generator (Llama 3.2 1B)
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-license: apache-2.0
+  - text-generation
+  - instruction-tuned
+  - llama
+  - unsloth
+  - lora
+  - linux
+  - command-generation
+license: other
 language:
-- en
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - custom
+base_model: unsloth/Llama-3.2-1B-Instruct
 ---
 
-# Uploaded finetuned  model
+### hrsvrn/linux-command-generator-llama3.2-1b
+
+Natural language → Linux command. A compact Llama 3.2 1B Instruct model fine‑tuned (LoRA) to turn plain‑English requests into correct shell commands.
+
+### TL;DR
+- Base: `unsloth/Llama-3.2-1B-Instruct`
+- Method: LoRA (r=16, alpha=16, dropout=0)
+- Context: 2048 tokens
+- Data: 8,669 pairs across 11 categories
+- Use cases: quick command lookup, learning CLI, automation
+
+## Run with Ollama (baby steps)
+
+1) Install Ollama: see `https://ollama.com/download`.
+
+2) Verify install:
+```bash
+ollama --version
+```
+
+3) Run the model interactively:
+```bash
+ollama run hrsvrn/linux-command-generator-llama3.2-1b
+```
+Then type a request, e.g.:
+- "List all files in the current directory with detailed information"
+- "Compress the file data.txt using bzip2"
+- "Find all .py files in the current directory and subdirectories"
+
+Press Ctrl+C to exit.
+
+4) One‑off (non‑interactive):
+```bash
+ollama run hrsvrn/linux-command-generator-llama3.2-1b -p "Display the first 5 lines of access.log"
+# Expected: head -n 5 access.log
+```
+
+5) Get command‑only answers (when needed):
+```bash
+ollama run hrsvrn/linux-command-generator-llama3.2-1b -p "Output only the command with no explanation. Show system information including kernel version"
+# Expected: uname -a
+```
+
+
+### Use a local GGUF with Ollama (fallback)
+If you have `model.gguf`, put it next to a `Modelfile`:
+
+```
+FROM ./model.gguf
+PARAMETER temperature 0.2
+PARAMETER top_p 0.9
+PARAMETER num_ctx 2048
+SYSTEM You are a Linux command generator. Output only the command with no explanation.
+TEMPLATE {{ .Prompt }}
+```
+
+Create and run:
+```bash
+ollama create linux-cmd-gen -f Modelfile
+ollama run linux-cmd-gen -p "Find all .py files recursively"
+# Expected: find . -name "*.py"
+```
+
+## Other ways to use (optional)
+
+### Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "hrsvrn/linux-command-generator-llama3.2-1b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else None)
+
+def generate_command(description: str) -> str:
+    messages = [{"role": "user", "content": description}]
+    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+    if torch.cuda.is_available():
+        inputs = inputs.to(model.device)
+        model = model.to("cuda")
+    outputs = model.generate(input_ids=inputs, max_new_tokens=64)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(generate_command("List all files in the current directory with detailed information"))
+# -> ls -la
+```
+
+### Unsloth
+```python
+from unsloth import FastLanguageModel
+
+model_id = "hrsvrn/linux-command-generator-llama3.2-1b"
+model, tokenizer = FastLanguageModel.from_pretrained(model_name=model_id, max_seq_length=2048)
+FastLanguageModel.for_inference(model)
+
+msgs = [{"role": "user", "content": "Compress the file data.txt using bzip2"}]
+inputs = tokenizer.apply_chat_template(msgs, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+output = model.generate(input_ids=inputs, max_new_tokens=32)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+# -> bzip2 data.txt
+```
+
+## Example prompts → commands
+- "Show system information including kernel version" → `uname -a`
+- "Find all .py files in the current directory and subdirectories" → `find . -name "*.py"`
+- "Display the first 5 lines of access.log" → `head -n 5 access.log`
+- "Change permissions of script.sh to make it executable for owner" → `chmod +x script.sh`
+- "Create a tar archive backup.tar containing all files in the documents folder" → `tar -cf backup.tar documents/`
+
+## Dataset (overview)
+8,669 input→command pairs across:
+- Compression & Archiving: bzip2, gzip, tar, zip
+- File & Directory: cd, cp, find, ls, mkdir, mv, pwd, rm, rmdir, touch
+- Permissions & Ownership: chgrp, chmod, chown
+- Viewing & Editing: cat, echo, head, less, tail, vim
+- Networking: curl, dig, host, ifconfig, ip, netstat, ping, ssh, wget
+- Package mgmt: apt, dpkg
+- Process mgmt: kill, killall, nice, pkill, renice
+- Search & Filter: awk, grep, locate, sed
+- System info/monitoring: df, du, free, top, uname
+- User/group: useradd, usermod, groupadd, passwd, sudo
+- Misc/system control: cron, systemctl, tmux, screen, service
+
+Format:
+```json
+{"input": "Describe what you want to do", "output": "linux_command_here"}
+```
+
+## Training details
+- Base: `unsloth/Llama-3.2-1B-Instruct`
+- LoRA on attention + MLP projections:
+  - r=16, lora_alpha=16, lora_dropout=0
+  - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
+- Max sequence length: 2048
+- SFT on responses only (TRL SFTTrainer), Unsloth-optimized
+- Example hparams: per‑device batch 2, grad accum 4, epochs 3, lr 2e‑4
+- Reference: Tesla P100 16GB (~45 minutes), ~2.8GB VRAM (adapters)
+
+## Safety and responsible use
+- Always inspect commands before executing.
+- Avoid destructive operations unless you fully understand consequences.
+- For apps, add denylists and validations (e.g., block `rm -rf /`, `mkfs`, `dd`).
+
+## Notes on GGUF
+- Works with `llama.cpp` and Ollama.
+- Typical memory (approx.): q4_k_s ~600MB, q4_k_m ~700MB, q8_0 ~1.1GB, f16 ~2.2GB.
+
+## License
+Derived from Meta Llama 3.2. Use must comply with the base model license. Check your deployment context for any additional constraints.
 
-- **Developed by:** hrsvrn
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
+## Citation
+```
+@software{hrsvrn_linux_command_generator_llama32_1b,
+  author  = {Harshvardhan Vatsa},
+  title   = {Linux Command Generator (Llama 3.2 1B)},
+  year    = {2025},
+  url     = {https://huggingface.co/hrsvrn/linux-command-generator-llama3.2-1b}
+}
+```
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Acknowledgements
+- Base: `unsloth/Llama-3.2-1B-Instruct`
+- Libraries: `unsloth`, `transformers`, `trl`, `accelerate`, `bitsandbytes`
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)