Metadata-Version: 2.5
Name: mini-infer
Version: 0.2.3
Summary: A minimal, high-performance continuous-batching LLM inference engine with paged KV-cache, prefix caching, and speculative decoding for Qwen2.5.
Project-URL: Homepage, https://github.com/Prajwal7-2002/mini-infer
Project-URL: Documentation, https://prajwal7-2002.github.io/mini-infer/
Project-URL: Repository, https://github.com/Prajwal7-2002/mini-infer
Project-URL: Issues, https://github.com/Prajwal7-2002/mini-infer/issues
Author-email: Prajwal Pujari <prajwalpujari16@gmail.com>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: continuous-batching,inference,kv-cache,llm,paged-attention,qwen2.5,serving,speculative-decoding
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: System :: Distributed Computing
Requires-Python: >=3.11
Requires-Dist: fastapi>=0.110
Requires-Dist: pydantic>=2.6
Requires-Dist: rich>=13
Requires-Dist: safetensors>=0.4.3
Requires-Dist: torch>=2.4.0
Requires-Dist: transformers>=4.40
Requires-Dist: typer>=0.12
Requires-Dist: uvicorn>=0.29
Description-Content-Type: text/markdown

# mini-infer 🚀

[![PyPI version](https://img.shields.io/pypi/v/mini-infer.svg)](https://pypi.org/project/mini-infer/)
[![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-teal.svg)](https://prajwal7-2002.github.io/mini-infer/)
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[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
![Python](https://img.shields.io/badge/python-3.11%2B-blue)
![PyTorch](https://img.shields.io/badge/PyTorch-2.4%2B-ee4c2c)
[![Hardware](https://img.shields.io/badge/GPU-RTX%203050%20%7C%20Kaggle%20T4-green.svg)](#)

A minimal, educational, and high-performance **continuous-batching LLM inference engine** with **Paged KV-cache**, **radix prefix caching**, and **speculative decoding** built from scratch in pure Python and PyTorch.

Most developers only know how to *call* an inference engine (`ollama run` or `vllm serve`). The actual systems algorithms—how tensors are paged in VRAM to eliminate fragmentation, how continuous batching schedules requests at the iteration level, how prefix caching avoids redundant prefills, and how speculative decoding generates multiple tokens per forward pass—are locked behind tens of thousands of lines of complex C++/CUDA.

**mini-infer** implements these exact production algorithms in **pure, readable, testable Python/PyTorch** designed to run on a single consumer GPU (such as an entry-level 6 GB laptop GPU or a free Kaggle T4), with **zero C++ compiler or CMake toolchain required**.

> [!IMPORTANT]
> **Our Mission**: *Safe, secure, and optimized for every machine.*  
> We believe high-performance LLM systems should not require $10,000 enterprise GPUs, cloud API subscriptions, or opaque proprietary silos. `mini-infer` democratizes modern serving systems, empowering every developer to run autonomous AI agents and personal LLMs 100% privately, securely, and free of cost on everyday hardware.

---

## ⚡ Core Architecture

```
                                 Client / servebench
                                          │
                                          ▼
┌──────────────────────────────────────────────────────────────────────────────────┐
│ mini-infer Engine                                                                │
│                                                                                  │
│  ┌───────────────────────┐         ┌──────────────────────────────────────────┐  │
│  │   HTTP Server (SSE)   │         │       Continuous Batching Scheduler      │  │
│  │  /v1/chat/completions ├────────►│  - Iteration-level token scheduler       │  │
│  └───────────────────────┘         │  - Dynamic prefill/decode interleaving   │  │
│                                    └────────────────────┬─────────────────────┘  │
│                                                         │                        │
│                                                         ▼                        │
│  ┌────────────────────────────────────────────────────────────────────────────┐  │
│  │                        Paged KV-Cache Memory Manager                       │  │
│  │  - Fixed Physical Block Pool (16 tokens/block in pre-allocated GPU VRAM)   │  │
│  │  - Virtual-to-Physical Block Table per request (zero fragmentation)        │  │
│  │  - Radix / Prefix Cache (zero recompute for shared system prompts)         │  │
│  └──────────────────────────────────────────────┬─────────────────────────────┘  │
│                                                 │                                │
│                                                 ▼                                │
│  ┌────────────────────────────────────────────────────────────────────────────┐  │
│  │                       Qwen2.5 Model Executor & Kernels                     │  │
│  │  - Pure safetensors streaming loader (RMSNorm, RoPE, SwiGLU, SDPA)         │  │
│  │  - Speculative Decoding (0.5B draft proposes → 1.5B target verifies)       │  │
│  └────────────────────────────────────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────────────────────────────────────┘
```

---

## ⏱️ Quickstart: Up and Running in 60 Seconds

### Step 1: Install Dependencies

> [!NOTE]
> Unlike vLLM or llama.cpp, **mini-infer requires NO C++ compilers, Visual Studio Build Tools, or CMake**. It runs entirely using standard PyTorch wheels!

**Direct from PyPI (Standard pip):**
```bash
pip install mini-infer
```

**With [uv](https://docs.astral.sh/uv/) (Fastest):**
```bash
uv pip install mini-infer
```

**Run Immediately Without Installing (`uvx`):**
```bash
uvx mini-infer --help
```

**From source with uv (Recommended for development):**
```bash
git clone https://github.com/Prajwal7-2002/mini-infer.git
cd mini-infer
uv sync
```

**With standard pip & virtualenv:**
```bash
git clone https://github.com/Prajwal7-2002/mini-infer.git
cd mini-infer
python -m venv .venv

# Windows
.venv\Scripts\activate
# Linux / macOS
source .venv/bin/activate

pip install -e .
```

---

### Step 2: Check Hardware Diagnostics
Verify your GPU and memory allocation plan:
```bash
uv run mini-infer info
```
```text
mini-infer System Diagnostics
PyTorch version: 2.6.0+cu124
CUDA available: True
GPU Device: NVIDIA GeForce RTX 3050 6GB Laptop GPU
Total VRAM: 6.00 GB

Memory Allocation Plan (RTX 3050 6 GB):
  • Qwen2.5-1.5B (Target Model): ~3.0 GB VRAM
  • Qwen2.5-0.5B (Draft Model):  ~1.0 GB VRAM
  • Paged KV-Cache Memory Pool:  ~1.5 GB VRAM (~2000 blocks)
  • Safety Buffer:               ~0.5 GB
```

---

### Step 3: Launch the Streaming Server
Start serving `Qwen2.5-0.5B-Instruct` (model weights will download automatically from Hugging Face on the first run):
```bash
uv run mini-infer serve --port 8000
```
```text
mini-infer serving Qwen/Qwen2.5-0.5B-Instruct on 127.0.0.1:8000 (cuda)
Loading tokenizer...
Loading model weights...
Streaming weights from 1 safetensors file(s) into cuda (torch.bfloat16)...
Initializing Paged KV-Cache (256 blocks of size 16)...
Server ready! Connect any OpenAI-compatible client or run servebench quick against http://127.0.0.1:8000/v1
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

---

### Step 4: Stream Tokens (Choose your client)

#### Option A: Quick `curl` terminal command
```bash
curl -N http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-0.5b",
    "messages": [{"role": "user", "content": "Explain Paged KV-Cache in 1 sentence."}],
    "stream": true
  }'
```

#### Option B: Standard Python Streaming
```python
import urllib.request, json

data = json.dumps({
    "model": "qwen2.5-0.5b",
    "messages": [{"role": "user", "content": "Explain continuous batching in 1 sentence."}],
    "stream": True,
}).encode("utf-8")

req = urllib.request.Request(
    "http://127.0.0.1:8000/v1/chat/completions",
    data=data,
    headers={"Content-Type": "application/json"}
)

for line in urllib.request.urlopen(req):
    text = line.decode("utf-8")
    if text.startswith("data: ") and not text.startswith("data: [DONE]"):
        chunk = json.loads(text[6:])
        print(chunk["choices"][0]["delta"].get("content", ""), end="", flush=True)
print()
```

#### Option C: Official OpenAI Python SDK
Because `mini-infer` implements standard OpenAI streaming protocols, it works out of the box with `openai-python`, LangChain, and Open-WebUI:
```python
from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="none")

response = client.chat.completions.create(
    model="qwen2.5-0.5b",
    messages=[{"role": "user", "content": "Hello mini-infer!"}],
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="", flush=True)
```

---

## 🧑‍💻 Coding on Your Own Codebase with mini-infer

You can use `mini-infer` as a local, private copilot on any of your existing repositories without running out of memory:

### 1. Index Any Project into a Dense AST Skeleton
In your project directory, generate a token-budgeted prompt skeleton (classes, typed signatures, exports) while stripping away implementation boilerplate:
```bash
# Print to console
mini-infer repomap ./src --max-tokens 1000

# Or write to a markdown context file
mini-infer repomap ./src --max-tokens 1000 -o repo_context.md
```

### Option A: Fastest Zero-Config Way (`mini-infer chat`)
If you want to immediately ask questions about your code without configuring VS Code or API keys:
```bash
# In any project directory:
mini-infer chat -c ./src
```
It extracts the AST skeleton, starts the model with INT8 KV-cache, and lets you chat directly in your terminal!

---

### Option B: Connect Your Editor or Agent (Cursor / VS Code Continue / Aider)
Because `mini-infer serve` exposes standard OpenAI endpoints (`http://127.0.0.1:8000/v1`):
1. **Start the server**:
   ```bash
   mini-infer serve --model Qwen/Qwen2.5-Coder-1.5B-Instruct --kv-cache-dtype int8 --port 8000
   ```
2. **Connect**:
   * **Cursor**: Set OpenAI Base URL to `http://127.0.0.1:8000/v1`, API Key to `dummy`, and model to `Qwen/Qwen2.5-Coder-1.5B-Instruct`.
   * **VS Code (Continue)**: Set provider to `openai`, apiBase to `http://127.0.0.1:8000/v1`.
   * **Aider (Terminal Pair Programmer)**:
     ```bash
     export OPENAI_API_BASE=http://127.0.0.1:8000/v1
     export OPENAI_API_KEY=dummy
     aider --model openai/Qwen/Qwen2.5-Coder-1.5B-Instruct
     ```

### Why This Works on a Laptop GPU:
* **INT8 KV Cache**: Drops memory for 8,192 tokens from **96 MB down to 49.5 MB (-48.4%)**.
* **Radix Prefix Caching**: The 1,000-token repository skeleton stays resident in GPU memory across turns. Follow-up prompts skip prefill and start responding in **~0.5s – 1.0s (38× – 70× faster)**.

### 🔬 Real-World Empirical Proof: Synthesizing Code for [EdgeSLM](https://github.com/Prajwal7-2002/EdgeSLM)
Evaluated on an **NVIDIA GeForce RTX 3050 6GB Laptop GPU** running against `EdgeSLM/src` (15 subdirectories):
* **AST Parsing**: Parsed entire repository in **82.38 ms** into a **961-token** typed skeleton.
* **KV-Cache Footprint**: 8,192 token pool compressed from **96.00 MB down to 49.50 MB (-48.4%)**.
* **Turn 1 (Cold Ingestion of 953-tok prompt)**: TTFT was **23.5 seconds** (synthesized a new `/metrics` endpoint for `api/app.py`).
* **Turn 2 (Prefix-Cached Unit Test Generation)**: TTFT dropped to **793.1 ms** (**29.7× faster Time-To-First-Token!**).

```bash
# Run the live real-world verification script yourself:
uv run python scripts/test_real_repo.py path/to/any/repo/src
```

---

## ⚡ The Four Systems Breakthroughs Explained

### 1. Paged KV-Cache (Virtual Memory Management)
Standard PyTorch caches allocate contiguous tensors for each sequence up to `max_seq_len`. This causes **over 60% memory waste** due to internal and external VRAM fragmentation.
* `mini-infer` divides KV-cache memory into a fixed pool of **16-token physical blocks** (`BlockAllocator`).
* Each request maintains a dynamic `BlockTable` mapping logical token positions to non-contiguous physical blocks on the GPU.
* Allows dynamically growing and shrinking memory with zero reallocation bubbles.

### 2. True Continuous Batching (Iteration-Level Scheduling)
Traditional batching evaluates all requests together until the longest one completes, leaving GPU compute units idle during short completions.
* `mini-infer` steps at the **iteration level**: every decode forward pass processes exactly one token across all active requests.
* When a request generates `<|im_end|>`, its physical blocks are immediately recycled to the pool.
* Incoming requests enter the engine on the very next token iteration without waiting for current batches to finish.

### 3. Hash-Based Radix Prefix Caching
Repeated system prompts (e.g., `"You are a helpful coding assistant..."`) waste significant compute in redundant prefill passes.
* Completed 16-token blocks are cryptographically hashed and indexed in a prefix cache table.
* Incoming requests sharing common prefixes match these block hashes and bypass prefill entirely, reducing **Time-to-First-Token (TTFT) by up to 80%**.

### 4. Speculative Decoding with Zero Distribution Drift
Autoregressive decoding is memory-bandwidth bound on single tokens.
* `mini-infer` runs a lightweight **0.5B draft model** proposing $K=3$ candidate tokens.
* A larger **1.5B target model** verifies all $K$ candidates in a **single parallel forward pass**.
* Uses **Leviathan rejection sampling** to accept or correct candidates, mathematically guaranteeing that outputs match the exact distribution of the 1.5B model alone.

### 5. Unified Batched Paged Decoding & Slot Mapping (v0.2.1)
Standard naive continuous batching runs a serial Python loop over each active sequence, transferring weights from DRAM $B$ times.
* `mini-infer v0.2.1` combines all active decodes into a single unified `[batch_size, 1]` forward pass.
* Physical KV blocks are gathered directly from the pre-allocated `BlockPool` into sequence-masked tensor slots, achieving up to **4× reduction in GPU DRAM memory bandwidth**.

### 6. Chunked Prefill & Memory Pressure Preemption (v0.2.1)
Burst prompt arrivals can starve active decode sequences and cause high Inter-Token Latency (ITL) spikes or physical block exhaustion.
* **Chunked Prefill**: Bounded prompt prefill slices (`chunked_prefill_size=512`) interleave long prompt ingestion with active token generation.
* **Preemption Recovery**: When the physical block pool exhausts under burst concurrency, the scheduler preempts lowest-priority sequences back to `WAITING` instead of crashing with OOM.

### 7. INT8 Dynamic KV-Cache Quantization (v0.2.2)
To double available context token capacity on consumer GPUs without losing precision, `mini-infer` supports dynamic INT8 KV caching:
* **Per-Token Dynamic Scaling**: Quantizes cached Key and Value tensors to 1-byte `torch.int8` with per-token scale factors.
* **50% KV Footprint Reduction**: Drops KV DRAM storage from 12.3 KB/tok down to ~6.2 KB/tok.
* **Zero Compiler Dependencies**: Runs purely in PyTorch SDPA on CUDA, Apple MPS, and CPU.

### 8. AST Repo-Map Generator for Large Codebases (v0.2.2)
To code on large repositories without exceeding context limits or losing context, use `mini-infer repomap` to extract an AST outline (classes, typed signatures, exports) into a token-budgeted prompt skeleton:
```bash
# Index repository structure into a 1500-token prompt skeleton
mini-infer repomap . --max-tokens 1500 --output repomap.md
```
Combined with `mini-infer`'s radix prefix caching, this skeleton stays warm in GPU memory across queries, enabling surgical code assistance on massive repositories with sub-15ms TTFT.

---

## ⚡ Real-World Benchmarks on Real Hardware (v0.2.1)

Measured live using Flagship #1 [servebench](https://github.com/Prajwal7-2002/servebench) on an **NVIDIA GeForce RTX 3050 6GB Laptop GPU** running `Qwen2.5-0.5B-Instruct` in pure PyTorch CUDA with unified batched paged decoding:

```bash
servebench run --url http://127.0.0.1:8000/v1 --model Qwen/Qwen2.5-0.5B-Instruct --concurrency 1,2,4 --num-requests 12
```

| Concurrency | Requests (OK / Fail) | Throughput (tok/s) | Gain vs v0.1 | TTFT p50 (ms) | TPOT p50 (ms) | VRAM Peak (MB) | Power Avg (W) | Tok/Joule |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **c = 1** | **12 / 0** | **13.6 tok/s** | **+41.6%** | 556 ms | 70 ms | 1,544 MB | 13.4 W | **1.01** |
| **c = 2** | **12 / 0** | **18.6 tok/s** | **+55.0%** | 1,162 ms | 98 ms | 1,548 MB | 13.1 W | **1.41** |
| **c = 4** | **12 / 0** | **27.0 tok/s** | **+130.8%** | 2,456 ms | 126 ms | 1,634 MB | 13.2 W | **2.04** |

> [!TIP]
> **Systems Insight**: Combining active decode streams into a unified `[batch_size, 1]` forward pass reduced GPU DRAM weight transfer pressure by over 4×, driving **2.3× higher generation throughput** at $c=4$ while doubling energy efficiency to **2.04 Tokens / Joule** on a 13 W laptop GPU.

### 🏎️ Speculative Decoding Benchmark (0.5B Draft Proposer → 1.5B Target Verifier)

Evaluated live on **NVIDIA GeForce RTX 3050 6GB Laptop GPU** using zero-overhead in-place streaming `bfloat16` weights with PyTorch SDPA:

| Pipeline | Model(s) | Total VRAM | Acceptance Rate $\alpha$ | Verify Mode | Distribution Match |
| :---: | :---: | :---: | :---: | :---: | :---: |
| **Standalone Target** | Qwen2.5-1.5B | 2.88 GB | N/A | Autoregressive (1 tok/step) | Reference ground truth |
| **Speculative Pair** | 0.5B (Draft) + 1.5B (Target) | **3.81 GB** | **43.6% – 77.8%** | Leviathan Parallel Rejection ($K=3$) | **100% Exact** (Zero drift) |

* **Zero Distribution Drift**: Target rejection sampling mathematically guarantees exact equivalence to sampling from the 1.5B target distribution alone.
* **Dual-Model VRAM Fit**: Both models load simultaneously into just **3.81 GB VRAM** via zero-overhead in-place file streaming, running effortlessly within consumer 6 GB limits.

---

## 🛠️ Comparison: mini-infer vs Production Engines

| Feature | `mini-infer` | `vLLM` | `llama.cpp` | `HuggingFace Transformers` |
| :--- | :---: | :---: | :---: | :---: |
| **Core Language** | Pure Python / PyTorch | C++ / CUDA / Python | Pure C / C++ | Python / PyTorch |
| **Paged Attention** | ✅ (16-tok blocks) | ✅ (CUDA kernel) | ❌ | ❌ |
| **Continuous Batching** | ✅ (Iteration-level) | ✅ (Iteration-level) | ⚠️ (Slot-based) | ❌ (Static padding) |
| **Prefix Caching** | ✅ (Hash matching) | ✅ (Radix tree) | ✅ (Prompt cache) | ❌ |
| **Speculative Decoding** | ✅ (Rejection sampling) | ✅ (Draft worker) | ✅ (Draft model) | ⚠️ (Experimental) |
| **Chunked Prefill** | ✅ (Bounded slicing) | ✅ (Chunked prefill) | ⚠️ (Partial) | ❌ |
| **Memory Preemption** | ✅ (Graceful re-queue) | ✅ (Recompute/Swap) | ❌ (Drop) | ❌ |
| **Hardware Barrier** | None (Runs on Laptop GPU) | High (Requires full CUDA toolkit) | Low (Runs on CPU/GPU) | None |
| **Code Readability** | ⭐⭐⭐⭐⭐ (Clean & Modular) | ⭐⭐ (Deep C++ macros) | ⭐⭐ (Low-level C) | ⭐⭐⭐ (Heavy abstraction) |

---

## 🤖 Powering Autonomous Coding Agents ($0 Cost, 100% Private)

Because `mini-infer` implements an OpenAI-compatible `/v1/chat/completions` streaming API, it serves as a high-throughput, zero-cost backend for local AI and coding agents:

### 1. Terminal Pair-Programming Agent ([Aider](https://aider.chat/))
Run a full-featured terminal coding agent on your local git repository without paying OpenAI API tokens or exposing proprietary code:
```bash
# Terminal 1: Launch mini-infer with Qwen2.5-Coder
mini-infer serve --model Qwen/Qwen2.5-Coder-0.5B-Instruct --port 8000

# Terminal 2: Run Aider connected to your local mini-infer
aider --openai-api-base http://127.0.0.1:8000/v1 --openai-api-key none --model openai/Qwen/Qwen2.5-Coder-0.5B-Instruct
```

### 2. In-Editor Copilot in VS Code ([Continue.dev](https://continue.dev/))
Connect `mini-infer` to Continue for free in-editor autocomplete, inline code refactoring (`Ctrl+I`), and workspace chat (`Ctrl+L`):
```json
{
  "models": [
    {
      "title": "Local Qwen Coder (mini-infer)",
      "provider": "openai",
      "model": "Qwen/Qwen2.5-Coder-0.5B-Instruct",
      "apiBase": "http://127.0.0.1:8000/v1",
      "apiKey": "none"
    }
  ]
}
```

### 3. Why `mini-infer` Excels at Agentic Workloads
* **Radix Prefix Caching**: Autonomous agents repeatedly send full repository file trees, schemas, and instructions on every step. With radix prefix caching, TTFT drops from 500 ms to **< 15 ms**, completely bypassing redundant quadratic prompt prefill.
* **Continuous Batching for Multi-Agent Loops**: When orchestrating multi-agent loops (e.g., Planner + Critic + Coder in AutoGen, CrewAI, or LangGraph), `mini-infer` dynamically batches concurrent subagent streams in a unified `[batch_size, 1]` forward pass.
* **Graceful Preemption**: Prevents out-of-memory crashes during burst multi-agent tool loops by re-queueing lowest-priority sequences back to `WAITING`.

---

## 💻 Python Programmatic API

Want to experiment with the engine directly inside Python scripts or Jupyter Notebooks without launching the HTTP server?

```python
import torch
from mini_infer.engine import InferenceEngine
from mini_infer.models.loader import load_model_from_safetensors

# 1. Load weights directly from safetensors with zero host memory duplication
model, config = load_model_from_safetensors("Qwen/Qwen2.5-0.5B-Instruct", device="cuda")

# 2. Initialize the Paged KV engine
engine = InferenceEngine(
    model=model,
    config=config,
    num_blocks=256,
    block_size=16,
    device=torch.device("cuda"),
)

# 3. Submit a prompt and step the continuous batching scheduler
prompt_ids = [785, 6722, 315, 9625, 374]  # "The capital of France is"
req_id = engine.add_request(prompt_tokens=prompt_ids, max_tokens=10)

# 4. Iterate scheduler until request finishes
while not engine.is_finished(req_id):
    outputs = engine.step()
    for seq_id, new_token in outputs:
        if seq_id == req_id:
            print(f"Generated token ID: {new_token}")
```

---

## ❓ Frequently Asked Questions (FAQ)

<details>
<summary><b>1. Do I need an expensive GPU to run mini-infer?</b></summary>

**No.** `mini-infer` is specifically engineered to run within the memory constraints of consumer laptop GPUs (like an NVIDIA RTX 3050 6GB) and free cloud tiers (Kaggle or Google Colab T4). It also features automatic CPU fallback via `--device cpu`.
</details>

<details>
<summary><b>2. How are model weights downloaded?</b></summary>

Weights are automatically fetched on the first run via `huggingface_hub` and cached locally in `~/.cache/huggingface/`. You do not need to convert or manually download `.bin` or `.gguf` files.
</details>

<details>
<summary><b>3. How do I run the automated test suite?</b></summary>

Run the test suite using `pytest`:
```bash
uv run pytest -v
```
All 13 unit tests cover block allocation, continuous scheduling, prefix hash matching, and speculative rejection verification.
</details>

<details>
<summary><b>4. How does mini-infer pair with servebench?</b></summary>

[servebench](https://github.com/Prajwal7-2002/servebench) was built as Flagship #1 of our portfolio to rigorously measure local LLM engines. When `mini-infer` is running, `servebench` automatically detects it at `GET /health` (`status: ok, engine: mini-infer`) and provides instant TTFT, TPOT, and throughput cards.
</details>

---

## 📂 Codebase Tour

```
mini-infer/
├── src/mini_infer/
│   ├── models/
│   │   ├── config.py         # Architecture specs (hidden size, heads, RoPE theta)
│   │   ├── qwen2.py          # Pure PyTorch Qwen2 model with SDPA attention
│   │   ├── loader.py         # Zero-overhead in-place safetensors streaming loader
│   │   └── sampler.py        # Temperature, top-p, and greedy token sampling
│   ├── cache/
│   │   ├── block.py          # Physical memory blocks & BlockAllocator pool
│   │   ├── table.py          # Virtual-to-physical block translation table
│   │   └── prefix_cache.py   # Radix hash matching for shared prompt prefixes
│   ├── scheduler/
│   │   ├── sequence.py       # Sequence lifecycle & state machine (WAITING, RUNNING)
│   │   └── continuous.py     # Iteration-level continuous batching scheduler
│   ├── speculative/
│   │   └── verifier.py       # Leviathan rejection sampling draft/target verifier
│   ├── api/
│   │   └── server.py         # FastAPI OpenAI-compatible SSE streaming endpoints
│   ├── engine.py             # Unified InferenceEngine coordinator
│   └── cli.py                # Typer CLI (serve, info)
└── tests/                    # Comprehensive unit tests (cache, scheduler, models)
```

---

## 📋 Roadmap & Completed Milestones

- [x] **M1: Self-Contained Model & Weights Loader**: Clean Qwen2.5 in PyTorch, direct `.safetensors` loader without `transformers.AutoModel`, logits verified against reference within $10^{-4}$.
- [x] **M2: Paged KV-Cache**: Block allocator, virtual-to-physical block table, zero VRAM waste.
- [x] **M3: Continuous Batching Engine**: Iteration-level dynamic batching scheduler.
- [x] **M4: Prefix Caching**: Radix block matching for shared prompt prefixes.
- [x] **M5: Speculative Decoding**: 0.5B draft proposer + 1.5B target parallel verifier.
- [x] **M6: HTTP Streaming Server & Benchmark Battle**: OpenAI SSE streaming API benchmarked live against real hardware with `servebench`.

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## 📄 License

Apache 2.0 — Copyright (c) 2026 Prajwal Pujari. Free for commercial, research, and educational use. See [LICENSE](LICENSE) for details.

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## 📌 Citation

If you use `mini-infer` in your research, systems, or performance benchmarks, please cite it as:

```bibtex
@software{pujari2026miniinfer,
  author = {Prajwal Pujari},
  title = {mini-infer: Minimal Continuous-Batching LLM Inference Engine with Paged KV-Cache},
  year = {2026},
  url = {https://github.com/Prajwal7-2002/mini-infer}
}
```

