Instructions to use navindbhudiya/qwen2.5-coder-7b-magento-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use navindbhudiya/qwen2.5-coder-7b-magento-v2 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("navindbhudiya/qwen2.5-coder-7b-magento-v2")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use navindbhudiya/qwen2.5-coder-7b-magento-v2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "navindbhudiya/qwen2.5-coder-7b-magento-v2"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "navindbhudiya/qwen2.5-coder-7b-magento-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use navindbhudiya/qwen2.5-coder-7b-magento-v2 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "navindbhudiya/qwen2.5-coder-7b-magento-v2"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default navindbhudiya/qwen2.5-coder-7b-magento-v2

Run Hermes

hermes

MLX LM

How to use navindbhudiya/qwen2.5-coder-7b-magento-v2 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "navindbhudiya/qwen2.5-coder-7b-magento-v2"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "navindbhudiya/qwen2.5-coder-7b-magento-v2"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "navindbhudiya/qwen2.5-coder-7b-magento-v2",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

qwen2.5-coder-7b-magento

A LoRA fine-tune of Qwen2.5-Coder-7B-Instruct (4-bit, MLX) specialized for writing idiomatic Magento 2 + Hyvä code. The adapter is fused into the base, so this is a self-contained model.

Current version: v5.1 (on main). Previous releases stay pinned as git tags — load an older version with revision="v5" / revision="v4" / revision="v2".

What's new in v5.1

A targeted data patch on top of v5, written against observed failure outputs:

CLI commands now register correctly — the CommandListInterface di.xml wiring was drilled with dedicated examples and a wrong→right contrast pair.
setup_version eliminated from generated module.xml (deprecated since declarative schema).
Fewer plugin result-type mistakes — more after-plugin examples where the $result type is not the obvious payload object (e.g. Quote::addProduct returns the quote Item), so the "mirror the return type" rule generalizes.
More total-collector coverage with rotated task phrasings, plus contrast pairs for the exact hallucinations seen in v5 output (Magento-1-style <sales><quote> XML nesting; inventing frontend asset files for a totals task).

Eval ladder (six multi-file scaffolding tasks, scored deterministically by file-set completeness, php -l, XML validation, and signature checks — fused weights, greedy decoding): v4 0/6 tasks (48/76 criteria) → v5 2/6 (69/74) → v5.1 3/6 (71/74).

Correction: an earlier revision of this card reported v5 at "2/6, 64/74" — those numbers were accidentally measured against the base model (an adapter-serving pitfall in mlx_lm.server). The fused v5 actually scores 69/74; the table above is measured on fused weights throughout.

What was new in v5

v5 retrained on an expanded dataset focused on complete multi-file generation — the main weakness of earlier versions, which tended to emit a single PHP class and drop its companion wiring XML:

File sets, not files: plugins are trained together with their di.xml, observers with events.xml, themes as the registration + theme.xml + composer.json trio, CLI commands with their CommandListInterface registration, cron jobs with crontab.xml.
Contrast pairs for recurring API mistakes: the after-plugin $result parameter is the intercepted method's return type; product status/visibility are EAV attributes (addAttributeToFilter), never raw columns on catalog_product_entity; a quote total collector must mutate the Total object; config XML is always wrapped in <config> with the correct xsi:noNamespaceSchemaLocation.

Developed by: Navin D. Bhudiya — AI Engineer (11+ years in e-commerce at scale).

⚠️ Apple Silicon only. This is an MLX model and runs via mlx-lm on Apple Silicon Macs. It is not loadable with transformers/vLLM/llama.cpp without conversion.

What it's for

A locally-running, open-source code assistant that writes house-style, modern Magento 2. A LoRA teaches style / conventions, not facts — so this model is good at how idiomatic Magento code is structured, and is best paired with RAG for factual grounding (see Limitations).

Consistently produces:

PHP 8 constructor property promotion + readonly, declare(strict_types=1), typed signatures
Plugins (not preferences) for behavior tweaks; observers + events.xml; correct di.xml wiring
HTTP-verb action interfaces (HttpGetActionInterface/HttpPostActionInterface), result objects
Service contracts (Api interfaces, repositories, SearchCriteria) over direct models
Declarative schema (db_schema.xml) + data/schema patches over InstallSchema
$escaper per context in .phtml; ViewModels over fat blocks
Hyvä = Alpine.js + Tailwind (no jQuery/Knockout)

…and avoids the base model's anti-patterns (e.g. setup_version, ObjectManager::getInstance(), Magento-1 app/code/local, and answering Magento questions in React/Vue/Java).

⚠️ Required inference settings

This model is served with sampling. Greedy decoding (temperature 0) can fall into repetition loops on long/complex generations.

temperature ≈ 0.3, top_p ≈ 0.95
repetition_penalty ≈ 1.1
stops on <|im_end|> (baked into config.json / generation_config.json — eos_token_id includes 151645)

Run with mlx-lm

pip install mlx-lm

mlx_lm.generate --model navindbhudiya/qwen2.5-coder-7b-magento-v2 \
  --prompt "Create an observer for catalog_product_save_after." \
  --temp 0.3 --top-p 0.95 --max-tokens 400

Serve an OpenAI-compatible API

mlx_lm.server --model navindbhudiya/qwen2.5-coder-7b-magento-v2 --port 8080 \
  --host 127.0.0.1 --temp 0.3 --top-p 0.95

curl -s http://127.0.0.1:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model":"navindbhudiya/qwen2.5-coder-7b-magento-v2",
  "messages":[{"role":"user","content":"Build a Hyva accordion component with Alpine.js and Tailwind."}],
  "max_tokens":512, "temperature":0.3, "top_p":0.95, "repetition_penalty":1.1
}'

Training (v5.1)

Method: QLoRA via mlx-lm — 8 layers, rank 8, dropout 0.1, optimizer AdamW (weight_decay 0.01), cosine-decay LR (peak 1e-4, 50-step warmup → 1e-6), batch-size 1, max-seq-length 2048, 720 iters. The validation curve is noisy on the small valid set, so the shipped checkpoint was selected by the downstream task eval (the six-task scorecard above), not val loss alone.
Data: 642 instruction→answer chat pairs (578 train / 64 valid) teaching generic, idiomatic Magento 2 conventions across backend, frontend/Hyvä, database, performance, error-handling, and tooling — including complete multi-file scaffolding exemplars and wrong→rule→correct contrast pairs (see above). Every example is length-validated with the real tokenizer + chat template (no silent truncation).
Regularization: fewer layers + dropout + weight decay + cosine decay keep the training loss from collapsing and hold validation flat — favoring generalization over memorization.
Hardware: trained on a 24 GB fanless Apple Silicon MacBook Air.

Limitations

Facts vs style: as a style-focused LoRA, it can confabulate long-tail facts. Observed cases where the raw model is unreliable (and RAG fixes them): the CLI "Area code is not set" API (it may invent AreaRegistryInterface::setCurrentArea; the real API is App\State::setAreaCode), CSRF (may invent interfaces instead of App\CsrfAwareActionInterface), choosing InputException vs LocalizedException for invalid input, and occasionally re-introducing an N+1 by calling getById() inside a loop over rows it already loaded. Pair with RAG for ground-truth API details; trust the model for structure/idioms.
Verify generated total collectors: v5.1 reliably emits the canonical collect(Quote, ShippingAssignmentInterface, Total) signature and $total mutation, but may still omit the fetch(Quote, Total) method that displays the line in the totals block — add it by hand if missing.
Greedy decoding loops: see Required inference settings — always use sampling + a repetition penalty.
Apple/MLX only.

Versions

revision	notes
`main` / `v5.1`	642-pair dataset, targeted patch (CLI registration, setup_version, $result types, collectors) — eval 3/6 tasks, 71/74 criteria
`v5`	618-pair dataset, multi-file scaffolding + contrast pairs — eval 2/6 tasks, 69/74 criteria
`v4`	478-pair dataset, anti-overfit recipe (8 layers, 500 iters) — eval 0/6 tasks, 48/76 criteria
`v2`	412-pair dataset, first public release

Privacy / data provenance

Trained only on anonymized, generic Magento patterns authored from general knowledge. No client/ company/project names, no proprietary business logic, no secrets/keys/credentials, no PII are present in the training data (validated with automated leak + denylist scans before training).

Author

Navin D. Bhudiya — AI Engineer with 11+ years shipping e-commerce at scale. Builds production RAG, LLM agents, and intelligent search on AWS (Claude API, Amazon Bedrock, LangGraph, vector DBs). AWS + Anthropic certified.

Designed, trained, evaluated, and released this Magento 2 + Hyvä code model (dataset curation → QLoRA fine-tuning on Apple Silicon → checkpoint selection → evaluation → RAG grounding). Connect: linkedin.com/in/navindbhudiya

License & attribution

Finetuned from Qwen2.5-Coder-7B-Instruct (Apache-2.0); base MLX conversion: mlx-community/Qwen2.5-Coder-7B-Instruct-4bit. This derivative is released under Apache-2.0.