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NGARiAI
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Model Details
Model Provider
NGARiAI
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Small models naturally struggle with the rigid syntax of function calling. NGARi solves this with a teacher→student pipeline:
markdown
Teacher (27B-class, e.g. qwen3.8-27B)│ generates multi-turn tool-calling scenarios:│ mock JSON schemas, user queries, correct API calls▼ngari-tool (1.5B)│ fine-tuned on perfectly structured examples▼Result: 100% tool-format accuracy at edge speed (runs on 8GB RAM)
The companion QA/safety model is NGARiAI/ngari-ft-distilled.
| Attribute | Value |
|---|---|
| Base model | Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0) — pinned in adapter_config.json |
| LoRA | rank 32, alpha 64, dropout 0.05, all linear projections |
| Synthetic data teacher | qwen3:8b (v1; 27B-class teacher planned for v2) |
| License | Apache 2.0 (NGARi-authored artifacts) |
| Hardware validated | aarch64 / NVIDIA Jetson AGX Orin, 8GB RAM, air-gap verified |
Google Gemma models were served only on NGARi hardware and were never used in NGARi training. All training used the Apache-2.0 Qwen2.5 lineage.
ngari-tool-stable_tool_eval.jsonjson
{"model": "ngari-tool:stable","num_examples": 20,"tool_detection_rate": 1.0,"tool_name_accuracy": 1.0,"params_validity_rate": 1.0,"tool_detected": 20,"name_correct": 20,"params_valid": 20,"avg_latency_sec": 3.17,"total_time_sec": 63.4}
| File | Purpose |
|---|---|
model-*.safetensors (+ config) | Merged full model — use with Transformers |
adapter_model.safetensors | PEFT LoRA adapter — apply on the base |
ngari-tool-q4_K_M.gguf / -f16.gguf | GGUF — use with Ollama / llama.cpp |
bash
# Ollama (GGUF)ollama create ngari-tool -f Modelfileollama run ngari-tool "What's the weather in Nairobi?"
python
# Transformers (merged)from transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("NGARiAI/ngari-tool")# PEFT adapter (apply on the base)from peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")adapter = PeftModel.from_pretrained(base, "NGARiAI/ngari-tool")
Trained and verified on user-owned edge hardware with zero cloud dependency. Verified air-gap (monitored via /proc/net/dev). "AI You Own. Completely."