PrismaCoder-3B

PrismaCoder-3B is a Qwen2.5-Coder-3B-Instruct fine-tune specialized for generating and editing Prisma ORM schemas and client code from natural-language instructions. It was trained with a full SFT → DPO → GRPO pipeline, using a verifier (gate) as both the data-quality filter and the reinforcement-learning reward signal.

Evaluation status: full 1,200-task PrismaBench has been run and the numbers below are real. They currently reflect static (V0) gate checking only — parses correctly, code fences balanced, referenced models/fields grounded in the schema — not verified compile-and-execute correctness against a real database (the full V1–V5 gate chain, which needs a Node ≥18 environment not available during this evaluation). See Evaluation and Limitations below before treating pass@1 as "code that works."

Model Details

• Base model: Qwen/Qwen2.5-Coder-3B-Instruct
• Parameters: ~3B
• Fine-tuning method: QLoRA (r=16, α=32, all linear projections) at each stage, LoRA weights merged into the base model between stages
• Training stages: SFT → DPO → GRPO (online RL, verifier reward) → iterative self-play RL round (attempted; see Limitations)
• Precision: 4-bit NF4 (bitsandbytes) during training, fp16/bf16 merged weights for release
• License: apache-2.0 (matches the base model)

Training Procedure

Stage	Method	Key hyperparameters
SFT	Completion-only loss, soft-ordering difficulty mixture	QLoRA r16/α32, lr 1e-4, cosine schedule, 3% warmup, effective batch 16, 3 epochs
DPO	Preference optimization on gate-differential pairs (static + on-policy mined)	β=0.05, lr 5e-7, 1 epoch, effective batch 16
GRPO	Online RL, reward = the same verifier used to filter training data	lr 1e-6, β=0.04, group size 2, effective batch 8 completions/step
RL round 2	Best-of-k self-play distilled into a short DPO round	Attempted; skipped this run (see Limitations)

All stages used seed 20260830 and ran on a single Kaggle T4 GPU (16GB) with gradient checkpointing and paged 8-bit AdamW.

Reward/gate design: every stage is filtered or scored by the same static verifier (LightValidator, "V0" in the project's internal gate numbering): it checks that generated Prisma schema/client code parses, has balanced code fences, and that referenced models/fields are actually grounded in the provided schema. It does not check that the generated client code compiles or executes correctly against a real database — that requires the full gate chain (V1–V5, Node-based), which did not run in this training environment. See Limitations.

Training Data

• Corpus: PrismaForge, a two-track dataset — a small mined track (real schema.prisma files and call sites crawled from permissively-licensed public repos) and a larger synthetic track (mechanically generated task/schema pairs across families, domains, and difficulty levels).
• Scale actually used for this model: ~3,000 of ~17,500 available finalized training records (MAX_TRAIN_SAMPLES=3000). This is smaller than the project's full-scale design target (120,000 synthetic records); results should be read as a proof-of-concept at this data scale, not the ceiling of the method.
• Contamination firewall: training records whose provenance hash matches a PrismaBench benchmark task are excluded from the training splits before SFT.

Evaluation

Benchmark: PrismaBench, a 1,200-task benchmark (categories T1–T12, difficulty levels L1–L3) built by the same project, with canary strings and a provenance registry used for the contamination firewall above.

Gate mode: static V0 gates only (static evaluation mode (no Node or --full-gates not passed) — printed by the evaluator on every run in this environment). This distinction matters: V0-only pass@1 measures well-formed, schema-grounded output; full-gate pass@1 (not yet run) would measure whether the generated code actually compiles, migrates, and executes correctly.

Stage	pass@1 (40-prompt internal proxy, V0 gates)
Base model (untrained)	0.700
+ SFT	0.975
+ DPO	0.975
+ GRPO	0.975
+ RL round 2	0.975

Full PrismaBench, N=1,200, static V0 gates — checkpoint: merged/grpo

Metric	Value
pass@1	0.9992
Wilson 95% CI	[0.9953, 0.9999]
Exact match	0.0175
CodeBLEU	0.9332

The low exact-match alongside near-perfect pass@1 is expected, not a red flag: pass@1 checks gate-level correctness (parses, balanced fences, schema-grounded references), not verbatim agreement with a single reference solution — code that is correct but phrased differently from the reference scores low on exact-match by design.

Per-category pass@1 (1,200 tasks):

Category	pass@1
T1_schema_greenfield	120/120 (1.00)
T2_schema_edit	120/120 (1.00)
T3_migration_plan	90/90 (1.00)
T4_client_crud	150/150 (1.00)
T5_complex_reads	139/140 (0.99)
T6_nested_writes_txn	120/120 (1.00)
T7_aggregations	100/100 (1.00)
T8_repo_completion	120/120 (1.00)
T9_bug_fix	100/100 (1.00)
T10_cross_file	60/60 (1.00)
T11_performance	60/60 (1.00)
T12_security	20/20 (1.00)

Error taxonomy: 1 failure classified E4; no other error classes observed across 1,200 tasks.

Which checkpoint this reflects: RL round 2 also completed successfully in training (self-play produced enough separable pairs this run, unlike an earlier attempt) and reached the same 0.975 proxy score as GRPO, but the 1,200-task benchmark above was run against the GRPO checkpoint (merged/grpo), not merged/rl2. The RL round 2 checkpoint has not yet been scored on the full benchmark — see Next Steps in the project repo before treating GRPO as the final, best checkpoint.

Baseline comparison: the only benchmark comparison currently valid for this card is against the untrained Qwen2.5-Coder-3B-Instruct base model, scored on the identical 1,200-task set (0.700 proxy pass@1 pre-training; full-benchmark base-model pass@1 not separately run). Comparisons against other released coding models are not included here because they have not been run through this same evaluator on this same held-out task set — a fair comparison requires that, and until it's done, any such claim on this card would be a number this project didn't actually measure.

Intended Use

• Generating and editing Prisma schema definitions and Prisma Client TypeScript code from natural-language instructions.
• Assistance with schema migrations, CRUD operations, and query composition against an existing Prisma schema.

Out-of-Scope / Limitations

• Static gates only, by default. Unless the model card above states full V1–V5 gates were used, evaluation numbers reflect syntactic/grounding correctness, not verified compile-and-run correctness.
• RL round 2 completed but is not the published checkpoint. In the training run that produced these results, RL round 2 (self-play distillation on top of GRPO) completed successfully and matched GRPO's proxy score, but has not yet been scored on the full 1,200-task benchmark. The checkpoint published here is GRPO, not RL round 2. If RL round 2 is later found to score higher on the full benchmark, this card and the published weights will be updated to reflect that — check the repo's commit history for the latest evaluated checkpoint.
• DPO's curated (non-mined) pair supply was very small in this training run (2 static gate-differential pairs); most of DPO's signal came from on-policy mining rather than the project's intended curated pair set.
• Domain-specific. This model is fine-tuned narrowly for Prisma ORM code generation and is not evaluated on, or intended for, general-purpose coding tasks outside that domain.
• Not evaluated for safety, security-sensitive code generation (e.g. auth/permission logic beyond the benchmark's T12_security category), or adversarial inputs.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sandeeprdy1729/PrismaCoder-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "You are a Prisma ORM code generation assistant."},
    {"role": "user", "content": "Add a `Comment` model related to an existing `Post` model, with a body field and a createdAt timestamp."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=640)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

A GGUF export (PrismaCoder-3B-f16.gguf) is also provided for llama.cpp/Ollama-based inference.

Training Infrastructure

Trained on Kaggle (single NVIDIA T4, 16GB), 4-bit QLoRA throughout, with step-level checkpointing every 10–25 steps and cross-session resume to fit within Kaggle's 12-hour session limit.

Citation

If you use this model, please cite the base model and note this fine-tune:

@misc{prismacoder3b,
  title  = {PrismaCoder-3B: QLoRA/DPO/GRPO fine-tune of Qwen2.5-Coder-3B for Prisma ORM code generation},
  author = {Thummala, Sandeep},
  year   = {2026},
  url    = {https://huggingface.co/sandeeprdy1729/PrismaCoder-3B}
}

Base model: Qwen2.5-Coder-3B-Instruct.