A code generation model that thinks before it codes.
Most code models optimize for autocomplete speed. This one was trained to reason like a staff engineer: explain the approach, discuss alternatives considered and rejected, flag production concerns, and then write the code. Trained on real architectural decisions from production systems -- not synthetic data, not textbook exercises.
What Makes This Different
Capability
What It Does
Reasoning first
Every response begins with explicit reasoning about why before what
Alternatives discussed
Names approaches it considered and explains why it rejected them
Go, Python, Java, and TypeScript -- trained on real patterns from each ecosystem
ADR-aware
Learned architectural decision records from production systems (e.g., "tools return structured data, agents apply intelligence")
Benchmark Results
Benchmark
Score
Details
Staff-Engineer Behavior
10.5 / 12 (87.5%)
Reasoning depth, alternatives analysis, production concern identification
Code Quality Suite
26 / 28 (93%)
7-test suite: type safety, concurrency, complete files, Redis atomicity, Spring Boot patterns, TypeScript types, architectural knowledge
Code Quality Breakdown
Test
Score
Go type safety (comma-ok assertions)
4/4
Go concurrency (mutex, goroutines)
4/4
Go complete compilable file
4/4
Python Redis atomicity (Lua scripts)
4/4
Java Spring Boot patterns
4/4
TypeScript discriminated unions
2/4
Architecture decision (ADR knowledge)
4/4
Total
26/28 (93%)
The model consistently produces responses that a senior engineer would recognize as staff-level thinking: the kind of code review comment that explains why the approach was chosen, not just what the code does.
Quick Start
With MLX (Apple Silicon)
python
1from mlx_lm import load, generate
23model, tokenizer = load("pikoder/pikoder-staff-engineer-14b")45messages =[6{"role":"system","content":"You are a staff-level software engineer. Think step by step before writing code."},7{"role":"user","content":"Write an HTTP client with retry and exponential backoff in Go"}8]910prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)11response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)12print(response)
With Transformers
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_id ="pikoder/pikoder-staff-engineer-14b"4tokenizer = AutoTokenizer.from_pretrained(model_id)5model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")67messages =[8{"role":"system","content":"You are a staff-level software engineer. Think step by step before writing code."},9{"role":"user","content":"Design a rate limiter using Redis Lua scripts in Python"}10]1112inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)13outputs = model.generate(inputs.to(model.device), max_new_tokens=2048)14print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
Usage Examples
1. Go: HTTP Client with Production-Grade Retry
Prompt:
Write an HTTP client with retry and exponential backoff
What you get: Not just a retry loop. The model reasons about jitter to prevent thundering herds, discusses context.Context for cancellation, considers whether to retry on 429 vs 500 status codes differently, and flags that retry without idempotency guarantees can cause duplicate side effects.
2. Python: Redis Rate Limiter with Atomicity
Prompt:
Design a rate limiter using Redis Lua scripts
What you get: The model explains why Lua scripts are necessary (atomicity across MULTI/EXEC is insufficient for read-modify-write), compares sliding window vs fixed window vs token bucket algorithms, identifies the race condition that plain Redis commands create, and produces a complete implementation with proper error handling for Redis connection failures.
3. Architecture: Tool vs Agent Responsibilities
Prompt:
Should MCP tools return recommendations or raw data?
What you get: A structured architectural analysis grounded in the ADR-001 principle learned during training: tools should return structured data while agents apply intelligence. The model discusses separation of concerns, testability implications, and the coupling risks of embedding decision logic in tool implementations.
Model Details
Architecture
Parameter
Value
Base model
Qwen3-14B (14.7B parameters)
Quantization
4-bit
Architecture
Qwen3ForCausalLM
Hidden size
5120
Layers
40
Attention heads
40 (8 KV heads, GQA)
Context length
40,960 tokens
Vocabulary
151,936 tokens
Training Configuration
Parameter
Value
Method
LoRA (Low-Rank Adaptation)
LoRA rank
16
LoRA alpha
32
LoRA scale
2.0
Dropout
0.1
Learning rate
1e-4
Batch size
2 (effective 8 with gradient accumulation)
Training iterations
200
Best checkpoint
Selected by validation loss (1.114)
Framework
MLX (mlx-lm 0.31.3)
Hardware
Apple M4 Pro, 48 GB unified memory
Peak memory
14 GB
Training Data
218 curated examples drawn from real production codebases:
Source
Description
16 ADRs
Architectural Decision Records documenting real design choices with context, alternatives, and consequences
Commit patterns, PR descriptions, and code review discussions from production repositories
Code patterns
Production implementations demonstrating idiomatic patterns in each language
Language distribution: Go (35%) / Python (35%) / Java (20%) / TypeScript (10%)
Every training example follows the same structure the model now produces: reasoning, alternatives considered, production concerns, then implementation. No synthetic data -- every example originated from real engineering decisions.
System Prompt
The model was trained with this system prompt baked into every example:
You are a staff-level software engineer. Think step by step before writing code.
For best results, include this system prompt (or a variation of it) when generating.
Intended Use
Best for:
Generating production-quality code with architectural reasoning
Learning idiomatic patterns in Go, Python, Java, or TypeScript
Not designed for:
Autocomplete / fill-in-the-middle (this is a chat model, not a code completion model)
Languages outside Go, Python, Java, TypeScript (it may work but was not trained for them)
Non-code tasks (summarization, translation, general chat)
Limitations
Training scale: 218 examples is small. The model inherits most of its capability from Qwen3-14B; the fine-tuning teaches style (reasoning-first responses) more than new knowledge.
Language coverage: Strongest in Go and Python (35% each). Java and TypeScript coverage is narrower. TypeScript type-level programming (discriminated unions, conditional types) is the weakest area.
Recency: Training data reflects codebases as of mid-2025. It does not know about libraries or APIs released after that date.
Quantization: 4-bit quantization trades precision for memory efficiency. Some numerical or edge-case responses may be less precise than the full-precision model.
Training Hardware
This model was trained entirely on consumer hardware: a single Apple M4 Pro with 48 GB unified memory. Peak training memory usage was 14 GB, completing 200 iterations in a single session. No cloud GPUs were used.
