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license: apache-2.0
tags:
- code
- agent
- merged
- fine-tuned
---
# Primus
**A coding model that fixes what developers actually complain about.**
Primus is a 295B-parameter coding model trained using the APEX method stack. Unlike frontier models optimized for benchmark scores, Primus is optimized for the five complaints developers have about AI coding assistants:
1. **Regression-free repair** — fixes the bug without breaking adjacent functionality
2. **Scope discipline** — changes only what you asked for, not 47 files
3. **Ask-vs-guess** — asks clarifying questions instead of guessing wrong
4. **API honesty** — never invents functions or libraries that don't exist
5. **Terseness** — minimal diffs, no essays
## Method
- **Training:** SFT → TIDE-DPO → RLVR sprint
- **Data:** 31,453 decontaminated samples (priors, CAT units, behavior demonstrations, TIDE pairs)
- **Hardware:** 8× RTX PRO 6000 (764GB VRAM)
## The APEX Stack
- **CAT** (Consequence-Augmented Training): Predicts blast radius before acting
- **TIDE** (Step-Anchored DPO): Assigns credit/blame at the exact causal step
- **SENTINEL**: Anti-reward-hacking verifier
- **AEGIS**: Adaptive best-of-N at inference (safest effective option)
- **SONAR**: External memory for long-horizon tasks
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"kovacreations/primus",
trust_remote_code=True,
device_map="auto",
torch_dtype="bfloat16"
)
tok = AutoTokenizer.from_pretrained("kovacreations/primus", trust_remote_code=True)
messages = [
{"role": "system", "content": "You are Primus, a coding assistant. Write clean, minimal code."},
{"role": "user", "content": "Fix the null pointer in auth.py"}
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(tok.decode(out[0], skip_special_tokens=True))
```
## Scorecard
Pre-registered 15-axis scorecard with all results published (wins and losses):
- 10 public benchmarks (Terminal-Bench, SWE-bench, etc.)
- 5 DevPain axes (regression, scope, ask, API honesty, terseness)
## Research Paper
See [PRIMUS_PAPER.md](PRIMUS_PAPER.md) for the full method description, training details, and analysis.
## Harness
Open-source evaluation harness: [github.com/kovalabs/nebula](https://github.com/kovalabs/nebula)
## License
Apache 2.0. Decontaminated training data. All losses published.
---
*Primus: first, do no harm to the codebase.*
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