bclermo/hermes-edge

🦊 Hermes Edge

On-device AI agent for iPhone 16 + Android — fully offline via LiteRT-LM.

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📱 Install on iPhone 16 (1 Tap)

https://huggingface.co/bclermo/hermes-edge/resolve/main/dist/hermes-mobile-270m-int4.litertlm

1. Open Google AI Edge Gallery app on your iPhone 16
2. Tap Import Model
3. Paste the URL above
4. The model auto-downloads and runs on A18 Pro Neural Engine

Requirements: iOS 18.2+, iPhone 16/16 Pro, LiteRT-LM runtime (bundled with Gallery).

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🧠 Architecture

Hermes Edge combines three advanced AI techniques:

1. DeepSeek-Style Reasoning

Chain-of-thought reasoning inspired by DeepSeek-R1 and DeepSeek-V4:

• Internal reasoning in <think>...</think> tags
• Step-by-step problem decomposition
• Self-verification of intermediate results
• Compatible with tool calling within reasoning traces

2. Hermes Tool Calling

NousResearch-compatible function calling format:

<tool_call>{"name": "calculator", "arguments": {"expr": "2+2"}}</tool_call>
<tool_response>{"name": "calculator", "content": "4"}</tool_response>

3. DSpark Speculative Decoding

Inspired by DeepSeek's DSpark framework — a lightweight draft model predicts K=4 tokens ahead, verified in a single pass by the main model. Up to 2.5× speedup with identical output quality (lossless).

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📊 Performance (iPhone 16 Pro — A18 Pro)

Model Variant	Speed	RAM	Size	DSpark Speedup
270M INT4	~55 tok/s	~180 MB	180 MB	2.1×
500M INT4	~40 tok/s	~320 MB	320 MB	2.3×
1B INT4	~25 tok/s	~650 MB	650 MB	2.5×

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🔧 Build Your Own Model

# Install
pip install litert-torch torch transformers sentencepiece

# Convert any HuggingFace model to .litertlm
litert-torch export_hf \
    --model=Qwen/Qwen2.5-0.5B-Instruct \
    --output_dir=./dist \
    --quantization=dynamic_wi4_afp32 \
    --cache_length=2048 \
    --prefill_lengths=32

Or use the Makefile:

make convert-270m   # Qwen2.5-0.5B → 270M INT4
make convert-500m   # Qwen2.5-1.5B → 500M INT4
make convert-1b     # Qwen3-0.6B → 1B INT4

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🚀 Quick Start

from hermes.litert_model import LiteRTModel
from hermes.agent import HermesAgent, AgentConfig
from hermes.chat_template import build_prompt, Message

model = LiteRTModel("dist/hermes-mobile-270m-int4.litertlm")
model.load()

agent = HermesAgent(model, config=AgentConfig(use_reasoning=True, use_speculative_decoding=True))
response = agent.run("What is 15% of 80?")
print(response)
# <think>Let me calculate 15% of 80...
# 10% of 80 = 8, 5% of 80 = 4, so 15% = 8 + 4 = 12</think>
# 15% of 80 is 12.

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🧩 Components

Module	Description
hermes/litert_model.py	LiteRT-LM runtime wrapper (Python)
hermes/agent.py	Agent loop: reasoning → tools → response
hermes/config.py	Model architecture configuration
hermes/chat_template.py	ChatML + tool calling format
scripts/convert_hf_to_litertlm.py	HF → .litertlm converter
scripts/deepseek_reasoning_template.py	DeepSeek-style reasoning templates
scripts/hermes_tool_format.py	Hermes tool calling format
scripts/dspark_draft.py	DSpark-inspired speculative decoding
hf-space/app.py	Gradio demo Space

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📋 Requirements

• Python 3.11+
• LiteRT-LM runtime (for inference)
• litert-torch (for conversion)
• torch + transformers + sentencepiece

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📄 License

Apache 2.0 — see LICENSE.

_{Hermes Edge · Built on Raven AI Ecosystem · Barry Clerjuste}