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Model: mecha-org/linux-command-generator-llama3.2-1b
Source: Original Platform

.gitattributes

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+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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Modelfile

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+
+FROM /kaggle/working/hrsvrn/linux-command-generator-llama3.1-1b/unsloth.F16.gguf
+TEMPLATE """{{ if .Messages }}
+{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
+{{- if .System }}
+
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question.
+{{- end }}
+{{- end }}<|eot_id|>
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
+{{- if and $.Tools $last }}
+
+Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+
+Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+
+{{ $.Tools }}
+{{- end }}
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+{{- if .ToolCalls }}
+
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+{{- else }}
+
+{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
+{{- end }}
+{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}
+{{- end }}
+{{- else }}
+{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|eom_id|>"
+PARAMETER temperature 1.5
+PARAMETER min_p 0.1

README.md

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+---
+title: Linux Command Generator (Llama 3.2 1B)
+tags:
+  - text-generation
+  - instruction-tuned
+  - llama
+  - unsloth
+  - lora
+  - linux
+  - command-generation
+license: other
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - custom
+base_model: unsloth/Llama-3.2-1B-Instruct
+---
+
+### mecha-org/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.
+
+## Video Demonstration of the model running on the Mecha Comet
+<video controls>
+  <source src="https://web-assets.mecha.so/hugging-face/mecha-command-generator-aug-10-2025.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+For more information of the Mecha Comet, our pocket little handheld computer - click <a href="https://mecha.so/comet">here</a>
+
+### 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 mecha-org/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 mecha-org/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 mecha-org/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 = "mecha-org/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 = "mecha-org/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.
+
+## 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/mecha-org/linux-command-generator-llama3.2-1b}
+}
+```
+
+## Acknowledgements
+- Base: `unsloth/Llama-3.2-1B-Instruct`
+- Libraries: `unsloth`, `transformers`, `trl`, `accelerate`, `bitsandbytes`
+

config.json

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+{
+    "model_type": "llama"
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.51.3"
+}

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+size 2471645448

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

unsloth.F16.gguf

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