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distil-labs/
Distil-gitara-v2-Llama-3.2-1B-Instruct

Name: Distil-gitara-v2-Llama-3.2-1B-Instruct
Availability: InStock

distil-labs/gitara-synthetic

meta-llama/Llama-3.2-1B-Instruct

finetune

llama3.2

text-generation-inference

endpoints_compatible

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distil labs

Homepage	GitHub	Hugging Face
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Llama-3.2-Gitara-1B

Gitara = git + ara (the parrot genus): your local stochastic parrot for git commands.

A 1B parameter function-calling model fine-tuned by Distil Labs to translate plain English into git commands. The smallest Gitara variant, optimized for resource-constrained environments while maintaining strong accuracy.

GitHub Demo and Code

Model Details


Developed by	Distil Labs GmbH
Model type	Causal language model, fine-tuned for function calling
Language	English
License	Llama 3.2 Community License
Fine-tuned from	meta-llama/Llama-3.2-1B-Instruct

Use Case

Given a natural language description of a git operation, the model outputs a structured JSON tool call that can be converted to an executable git command.

Supported commands: status · add · commit · push · pull · branch · switch · restore · merge · stash · rebase · reset · log

Example

Input:

push feature-x to origin, override any changes there and track it

Output:

{"name": "git_push", "parameters": {"remote": "origin", "branch": "feature-x", "force": true, "set_upstream": true}}

Resulting command:

git push origin feature-x --force --set-upstream

More Examples

Query	Tool Call Output
`what's in the latest stash, show diff`	`{"name": "git_stash", "parameters": {"action": "show", "patch": true}}`
`undo last commit but keep the changes`	`{"name": "git_reset", "parameters": {"mode": "soft", "ref": "HEAD~1"}}`
`show 8 commits for current branch with graph`	`{"name": "git_log", "parameters": {"limit": 8, "graph": true}}`
`merge vendor branch preferring ours`	`{"name": "git_merge", "parameters": {"branch": "vendor", "strategy": "ours"}}`

Training

The model was trained using knowledge distillation from the teacher model GPT-OSS-120B.


Training method	LoRA fine-tuning
Seed examples	~100 (manually validated)
Synthetic examples	10,000
Teacher model	GPT-OSS-120B

Training Process

Created ~100 seed examples covering all 13 git commands with realistic query phrasings
Expanded seed data to 10,000 synthetic training examples using the Distil Labs platform
Fine-tuned Llama 3.2 1B Instruct using LoRA
Validated on held-out test set

Training data and configuration available in the GitHub repository.

Evaluation

Evaluated on 50 held-out test examples. Accuracy is measured by parsing outputs into normalized Python dicts and comparing for structural equality.

Model	Parameters	Accuracy
GPT-OSS-120B (teacher)	120B	0.92 ± 0.02
Llama 3.2 3B Instruct (tuned)	3B	0.92 ± 0.01
Llama 3.2 1B Instruct (tuned)	1B	0.90 ± 0.01
Llama 3.2 3B Instruct (base)	3B	0.12 ± 0.05
Llama 3.2 1B Instruct (base)	1B	0.00 ± 0.01

The tuned 1B model achieves 0.90 accuracy while being 120x smaller than the teacher. The base 1B model completely fails (0.00 accuracy), confirming that fine-tuning is essential.

When to Use 1B vs 3B

Choose 1B	Choose 3B
Memory-constrained devices	Maximum accuracy needed
Faster inference required	Complex or ambiguous queries
0.90 accuracy is acceptable	Edge cases matter

How to Use

With Ollama (Recommended)

bash

# Download model
huggingface-cli download distil-labs/Distil-gitara-v2-Llama-3.2-1B-Instruct --local-dir distil-model

# Build with Ollama
cd distil-model
ollama create gitara-1b -f Modelfile

# Run
ollama run gitara-1b "show staged changes with diffs"

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "distil-labs/Distil-gitara-v2-Llama-3.2-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# See GitHub repo for full tool-calling implementation

For complete usage instructions, see the GitHub repository.

Inference Speed

Faster than the 3B variant due to smaller size. Suitable for on-device deployment on mobile or embedded systems.

Limitations

Accuracy is 0.90, meaning approximately 1 in 10 queries may produce incorrect output
Limited to the 13 supported git commands and their common options
Does not support git checkout (use switch and restore instead)
Single-turn only; does not support multi-step workflows
May struggle more with ambiguous or complex queries compared to 3B variant

Model Sources


Homepage	https://distillabs.ai
Repository	https://github.com/distil-labs/distil-gitara
Blog post	https://distillabs.ai/blog/gitara
Contact	contact@distillabs.ai

Related Models

Llama-3.2-Gitara-3B — Larger variant (0.92 accuracy)

Citation

bibtex

@misc{gitara2025,
  author = {Distil Labs},
  title = {Gitara: A Function-Calling Git Agent},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/distil-labs/Distil-gitara-v2-Llama-3.2-1B-Instruct}
}