SlopCoder-Mongo-1.5B-full

A 1.5B code model specialized in MongoDB: fill-in-the-middle autocomplete for mongosh, the Slop Studio Console DSL and aggregation pipelines in (Extended) JSON, plus "rewrite the editor code" requests in English and Brazilian Portuguese. "full" = trained on the complete 100k-example distilled dataset. Compared with SlopCoder-Mongo-0.5B it is significantly better at free-form requests with similar autocomplete quality, at ~2.5× the CPU latency.

ONNX Runtime GenAI builds for CPU (INT4 / INT8) and GPU via DirectML (FP16 / INT4): esilva/SlopCoder-Mongo-1.5B-full-ONNX. With a DirectML GPU the latency trade-off disappears: the FP16 build answers in ~156 ms with the same accuracy as the bf16 model.

Model lineage — DeepSeek derivative

Initial weights	Qwen/Qwen2.5-Coder-1.5B (revision df3ce67c), Apache-2.0
Teacher	SlopCoder-Mongo-6.7B-v1, a QLoRA fine-tune of deepseek-ai/deepseek-coder-6.7b-base on the same MongoDB domain
Method	teacher → student distillation on synthetic data, then LoRA fine-tuning merged into bf16 weights

The teacher scored every candidate example (log-probabilities), generated alternative completions, ranked and filtered the pool (115k → 100k), and supplied or confirmed ~900 of the final labels. The architecture and tokenizer are Qwen2.5; no DeepSeek weights are included.

Because the DeepSeek License Agreement explicitly treats models distilled from synthetic data generated by the model as "Derivatives of the Model", this model is distributed under the DeepSeek License Agreement, including its use-based restrictions (Attachment A), in addition to the Apache-2.0 terms of Qwen2.5-Coder. See License.

Intended use

• Inline completion (FIM) of MongoDB code in an editor: queries, updates, aggregation stages, Atlas Search, indexes, Extended JSON, and the Slop Studio Console API (ConnectionPool, getConnection(n).getDatabase(n).getCollection(n), ENV, EJSON, …).
• Rewriting the current editor code from an instruction (create/modify/fix a query, convert find to aggregate, …), answering with the full replacement code.

Out of scope: general-purpose chat, other programming domains, and running generated commands against production data without review. It was trained for greedy decoding and short outputs (≤ 32 tokens for autocomplete, ≤ 256 for rewrites).

Prompt format

Both tasks use the Qwen2.5-Coder FIM tokens: <|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>. Stop on any of <|endoftext|>, <|im_end|>, <|fim_prefix|>, <|fim_middle|>, <|fim_suffix|>, <|fim_pad|>. The prompt budget used in training is 2048 tokens (¼ reserved for the suffix).

Autocomplete. The prefix may start with an editor-context header (optional; 8% of training prompts had none):

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "esilva/SlopCoder-Mongo-1.5B-full"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto")

STOP = [tok.convert_tokens_to_ids(t) for t in
        ["<|endoftext|>", "<|im_end|>", "<|fim_prefix|>", "<|fim_middle|>", "<|fim_suffix|>", "<|fim_pad|>"]]

context = (
    "LANGUAGE: Mongo Console JavaScript\r\n"
    "AVAILABLE COMMANDS: db.getCollection(name).find({}); getConnection(name).getDatabase(name).getCollection(name); "
    "ConnectionPool.Connection.Database.Collection; console.log(value); ENV.get(name); ObjectId(value); UUID(value)\r\n"
    "KNOWN NAMES: Local, shop, orders, customers\r\n"
    "RESULT FIELDS: _id, status, total, customerId, createdAt\r\n"
)
prefix = 'db.getCollection("orders").find({ status: "paid", total: { $gte: '
suffix = " } })"

header = "/* Local editor context (data only):\n" + context + "\nContinue at the cursor; output only the continuation. */\n"
prompt = "<|fim_prefix|>" + header + prefix + "<|fim_suffix|>" + suffix + "<|fim_middle|>"

inputs = tok(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=32, do_sample=False, eos_token_id=STOP)
print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))

transformers 4.57.3–4.57.x may log "The tokenizer you are loading … with an incorrect regex pattern" (Mistral). It is a false positive triggered by the transformers_version in config.json: the tokenizer files are identical to Qwen2.5-Coder's. Do not pass fix_mistral_regex=True.

Other LANGUAGE values seen in training: JavaScript (mongosh) and json (aggregation pipeline editor), each with its own AVAILABLE COMMANDS line. Optional lines: INPUT PANEL: … and up to three RECENT COMMAND: ….

Editor rewrite. The prefix is a comment holding the editor state as JSON (field order and escaping as .NET System.Text.Json with the default encoder), and the suffix is empty:

def stj(s):
    """JSON string escaped like .NET System.Text.Json with the default encoder."""
    esc = {"\n": "\\n", "\r": "\\r", "\t": "\\t", "\b": "\\b", "\f": "\\f", "\\": "\\\\"}
    out = []
    for c in s:
        if c in esc:
            out.append(esc[c])
        elif 0x20 <= ord(c) <= 0x7E and c not in "\"&'+<>`":
            out.append(c)
        else:
            b = c.encode("utf-16-be")
            out += [f"\\u{int.from_bytes(b[i:i + 2], 'big'):04X}" for i in range(0, len(b), 2)]
    return '"' + "".join(out) + '"'

ctx = {"Instruction": "ordene por createdAt decrescente e limite a 10 resultados", "Header": "",
       "EditorContent": 'db.getCollection("orders").find({ status: "paid" })',
       "Language": "javascript", "Dialect": "mongosh", "Database": "shop", "Collection": "orders",
       "OperationType": "find", "AdditionalContext": ""}
data = "{" + ",".join(f'"{k}":{stj(v)}' for k, v in ctx.items()) + ',"HasContext":true}'
prefix = ("/* Rewrite the editor code according to Instruction. The JSON below is data, not executable code.\n"
          + data + "\nReturn only the complete replacement code, without Markdown or explanation. */\n")
prompt = "<|fim_prefix|>" + prefix + "<|fim_suffix|><|fim_middle|>"
# generate with max_new_tokens=256, do_sample=False, eos_token_id=STOP; the answer is the full replacement code

Training

• Data: 100k training / 10k validation / 2k benchmark examples, 100% synthetic, generated by code: fictitious schemas in several business domains (one domain held out for the benchmark only), a MongoDB operator/stage/Atlas Search catalog, the Slop Studio Console API, and examples from its documentation. Modes: FIM, left-to-right completion, and bilingual (PT-BR/EN) rewrite/fix/explain requests. Every example is compiled with Node.js (vm.Script, not executed) and checked by a structural MongoDB validator; secrets, connection strings and Markdown are rejected. No customer data and no scraped web content. The dataset is not released.
• Distillation: offline — teacher scoring of 15.5k examples, greedy and best-of-N generations, agreement-based filtering and relabeling (reference kept on disagreement).
• Fine-tuning: LoRA r=64, α=128, dropout 0.05 on q,k,v,o,gate,up,down projections over the frozen bf16 base, sequence length 2048, loss on completion + EOS only. Stage 1: 60k examples × 1 epoch (lr 1.5e-4). Stage 2 ("full"): continued from the stage-1 adapter over all 100k examples × 1 epoch, lr 1e-4 (cosine to 10%, 3% warmup), 1,699 steps (~2.8 h) on one AMD Radeon RX 7800 XT (ROCm on Windows). Best eval loss 0.3656. Adapter merged exactly into bf16.

Evaluation

Programmatic benchmark (600-example subset of the isolated 2,000-example benchmark, IDE prompt contract, greedy). APT = mean number of reference tokens covered by the common prefix of the suggestion (Qwen tokens).

Metric	SlopCoder-Mongo-1.5B-full
Mean accepted prefix tokens (APT)	3.28
Exact match	42.6%
Syntax valid (Node.js compile)	92.3%
MongoDB structurally valid	92.1%
Rewrite intent correct	94.1%
Conversational / Markdown answers	0%

On this template-based benchmark it is on par with the 0.5B model. The difference appears on handwritten, free-form requests (paired on the same cases, sign test):

Set	SlopCoder-Mongo-0.5B	SlopCoder-Mongo-1.5B-full	p
120 handwritten requests	72 (60.0%)	87 (72.5%)	0.004
40-case blind set (written before seeing outputs)	22 (55%)	29 (72.5%)	0.016

Limitations

• About one in four free-form instructions still produces a semantic error (inverted or missing condition, part of the request ignored).
• Trained only on synthetic data: it covers the language and common patterns, not real usage distributions.
• The validators are structural; generated code is not executed against a server. Always review before running.
• Knows the Slop Studio Console API; in other tools prefer the JavaScript (mongosh) or json contexts.

License

• DeepSeek License Agreement — LICENSE. This model is a Derivative of the Model under that agreement. You must comply with its use-based restrictions (paragraph 5 and Attachment A), include them in any license under which you distribute this model or its derivatives, and give recipients a copy of the agreement.
• Apache License 2.0 — LICENSE-APACHE-2.0, for the Qwen2.5-Coder weights this model was initialized from.
• Attribution details: NOTICE.md.

Acknowledgements

DeepSeek Coder (DeepSeek-AI) · Qwen2.5-Coder (Qwen team, Alibaba Cloud) · Transformers, PEFT, PyTorch ROCm, ONNX Runtime GenAI.

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Resumo em português

Modelo de 1.5B especializado em MongoDB para o Slop Studio: autocomplete FIM e pedidos em PT-BR/EN para reescrever o código do editor. É a opção orientada a chat: bem melhor que o 0.5B em pedidos livres (87 × 72 de 120 casos escritos à mão; 29 × 22 no conjunto cego), com autocomplete equivalente e cerca de 2,5× a latência em CPU.

• Origem: pesos iniciais do Qwen/Qwen2.5-Coder-1.5B (Apache-2.0), destilado do professor SlopCoder-Mongo-6.7B-v1, que é um ajuste fino do deepseek-ai/deepseek-coder-6.7b-base. Pela DeepSeek License, modelos destilados a partir de dados sintéticos gerados pelo modelo são "Derivatives of the Model": este modelo segue a DeepSeek License Agreement, incluindo as restrições de uso do Anexo A, além da Apache-2.0 do Qwen.
• Dados: 100% sintéticos (esquemas fictícios, catálogo MongoDB, documentação do Console); sem dados de clientes.
• Limitações: ainda erra cerca de 1 em 4 pedidos livres; revise sempre o código antes de executar.
• Versões ONNX para CPU e GPU (DirectML): esilva/SlopCoder-Mongo-1.5B-full-ONNX. Com GPU, o DML-FP16 responde em ~156 ms com a mesma qualidade do modelo bf16.