The model converts natural-language feature requests into validated YAML specifications that conform to the COMMAND_RUNWAY methodology. Each spec contains:

task_id, summary, depends_on, local_goals, global_goals_refs, context
Every local_goal has an Inspect → Create/Modify → Verify verification flow
Specs pass a hardened validator (canonical vocabulary, near-duplicate detection, YAML safety)
Specs are scored against runbook-readiness criteria (hard gate: missing Inspect/Create/Verify stages = 0.0)

Training Details

Parameter	Value
Base model	`unsloth/Qwen2.5-Coder-7B-Instruct`
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.1
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs	3
Learning rate	2e-4
Batch size	1 (effective: 4 via gradient accumulation)
Max sequence length	2048
Quantization	4-bit NF4
Optimizer	adamw_8bit
LR scheduler	cosine
Warmup ratio	0.1

Training Data

Source: 475 seed prompts across 21 feature categories
Generation: Ollama (qwen2.5-coder:7b-instruct) at temperature 0.2
Validation: Hardened YAML spec validator (78 test cases)
Scoring: Runbook scorer with hard gate (0.75 threshold)
Format: Chat format (system + user + assistant turns)

Evaluation

See data/eval_results.json after running make eval-model.

Metrics:

Validation rate: percentage of specs that pass the hardened validator
Score pass rate: percentage of specs scoring >= 0.75 on the runbook scorer
Target: >80% score pass rate (held-out prompts)

Usage

Ollama (GGUF)

bash

# Download GGUF from this repo's models/ directory
ollama create promex -f models/qwen2.5-coder-7b-promex-gguf/Modelfile
ollama run promex

HuggingFace Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct", torch_dtype="auto")
model = PeftModel.from_pretrained(base, "moinonin/qwen2.5-coder-7b-promex")
tokenizer = AutoTokenizer.from_pretrained("moinonin/qwen2.5-coder-7b-promex")

messages = [
    {"role": "system", "content": "You are a precise specification generator. Output ONLY a YAML document."},
    {"role": "user", "content": "Add a POST /health endpoint that returns 200 OK"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Trained on a synthetic corpus generated by the base model itself — quality is bounded by the base model's spec-generation ability
Specs are scoped to a single-file, single-feature granularity (not multi-stage epics)
Context is fixed to TypeScript/Express/Prisma/Vitest stack
GGUF quantization (q4_k_m) introduces minor quality degradation vs the 16-bit merge

Ethical Considerations

This model generates structured specifications, not executable code
All generated specs must pass the hardened validator before use
Human review is required before feeding specs into a COMMAND_RUNWAY executor

Citation

bibtex

@misc{githeri-promex,
  title={ProMex: From Natural Language to Runbook-Ready YAML Specifications},
  author={Githeri},
  year={2026},
  url={https://github.com/nickrotich/githeri}
}

License

Apache 2.0 — same as the base Qwen2.5-Coder-7B-Instruct model.

moinonin/qwen2.5-coder-7b-promex-gguf

qwen2.5-coder-7b-promex

Model Description