Hydra Nano Git (hydra-nano-git)

Hydra Nano Git is an enterprise-grade, specialized local coprocessor trained to handle read-only repository inspection, history auditing, branch analysis, and diff navigation for autonomous frontier coding agents (such as Claude Code, Antigravity, and Codex).

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🏛️ Executive Summary & Value Proposition

Frontier coding agents waste 70% to 90% of their expensive context window and API rate limits dumping voluminous raw CLI outputs (git log, git diff, git status).

Hydra Nano solves this by acting as a deterministic local coprocessor:

1. Zero Shell Command Risk: The model never generates raw shell commands. It selects strictly bounded read-only probes from a hardened menu (status, log, diff, show, blame, branches, stash, worktrees).
2. Grounded Runtime Verification: Strict parameter validation ensures branch names, commit hashes, authors, and file paths exist before execution.
3. Extreme Token Savings: Preserves high-value frontier context while maintaining 100% factual fidelity.
4. Sub-second Local Inference: ~140ms per task on Apple Silicon M-series GPUs and modern local accelerators.

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📊 Proven Benchmark & Evaluation

Evaluated across the sealed 1,000-task git1 benchmark and development suites:

Metric	Score	Industry Standard
Plan Accuracy	83.0%	Plan selection for diverse git queries
Sealed Task Solved Rate	77.0%	Usage-weighted solved accuracy on sealed benchmark
High-Frequency Query Solved Rate	90% - 100%	branch_current (100%), last_commit (100%), modified_files (90%), staged_files (90%)
Inference Latency	~140ms	Local Apple Silicon M-series GPU
Token Reduction	73.3% - 88.4%	Average context reduction compared to raw git dumps

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🚀 Quickstart & Usage

Via Ollama

ollama run rahul05ranjan/hydra-nano-git

Via Hydra CLI

hydra ask --head git "which branch am I on and what files are modified?"

Via Python (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "rahul05ranjan/hydra-nano-git"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

prompt = "Which commits did alice touch last week?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🔒 Security & Safe Execution Policy

• Read-Only Guarantee: Does not support state-mutating operations (git commit, git push, git rebase, git reset).
• No Prompt Injection Shell Escalation: Command generation is entirely programmatic; model outputs strictly conform to a JSON probe schema.

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📜 Specifications

• Base Architecture: Qwen3.5-0.8B Dense Hybrid Attention
• Parameters: 752.39M
• Max Context Window: 262,144 tokens (8,192 default in Ollama)
• Precision: BF16 / INT8 Quantized
• License: Apache 2.0