01
Generate regression tests.
Send a function, the current tests and a concrete failure case. Ask for new tests without modifying the implementation, then run them against both the broken and corrected versions.
Qwen2.5-Coder-7B
Generate code and tests.
Qwen2.5-Coder-7B-Instruct is an instruction model built for code. Use an AI Fabric worker to draft tests, propose focused refactors and explain selected files, then review each result with your existing development tools.
€5 in free credits for every new customer.
Plan your deploymentAI Fabric access is opening gradually. GPU configuration is confirmed during onboarding.
Tests, refactoring and code explanations.
Choose a bounded development task with a checkable result. A useful code worker returns a proposal your existing review and test process can accept or reject.
02
Draft an isolated refactor.
Provide a single module and its public interface. Request a patch that preserves behaviour, and use the existing tests to decide whether it is worth keeping.
03
Explain legacy code inside your editor.
Return a short explanation grounded in the selected function and its callers. Keep file and line references so the developer can verify the description in place.
Model capabilities and upstream setup: Qwen2.5-Coder-7B publisher reference. Reviewed .
Deployment preparation
Load Qwen2.5-Coder-7B in Python.
The Instruct checkpoint accepts a conversational request through its chat template. Use it for bounded code tasks; an editor integration also needs file selection, output handling and a way to run checks.
- Load the Instruct checkpoint with Transformers and a validated quantization configuration.
- Start with one function and a short output budget; save the prompt and model revision with the proposed patch.
- Expose the tested function through the onboarding runtime, then connect a script or assistant tool to it.
import torch
from transformers import (
AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,
)
model_id = "Qwen/Qwen2.5-Coder-7B-Instruct"
quant = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
model_id, quantization_config=quant, device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [{"role": "user", "content":
"Write pytest tests for a function that retries at most three times."}]
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(
**inputs, max_new_tokens=512, do_sample=True,
temperature=0.7, top_p=0.8,
)
print(tokenizer.decode(
output[0][inputs.input_ids.shape[-1]:],
skip_special_tokens=True,
))Install compatible PyTorch, Transformers, Accelerate and bitsandbytes. This is a quantized inference example to validate, not a tested AI Fabric deployment command. Transformers quantization setup ↗
GPU memory & performanceAbout 15 GB of BF16 weights leave limited headroom on a 16 GB GPU. The 4-bit example reduces weight memory, while context length and concurrent jobs still need their own budget.
In your applicationRun generated tests in an isolated project environment. Do not let a patch request silently install dependencies or execute unrelated repository commands.
AI Fabric availability & costRTX A4500 (20 GB) and RTX 2000 Ada (16 GB) are the GPU options. Access, runtime settings and billing are confirmed during onboarding. No Blade latency or throughput benchmark is published for this configuration.
Delegate test writing from your coding assistant.
A configured skill or API runner can hand a selected task to your Coder deployment, then return the proposal to the primary assistant. Keep the first integration narrow: generate tests, review the diff and run the suite before accepting changes.
01 / InputFunction + acceptance criteria
02 / ProcessCoder proposes tests
03 / ResultAssistant reviews and runs checks
Qwen2.5-Coder-7B
deployment FAQ.
Is this autocomplete or an instruction model?
This guide uses Qwen2.5-Coder-7B-Instruct. It is designed for instructions such as writing tests or explaining code; do not assume an autocomplete protocol without building that integration.
Can I delegate work from Claude Code or another coding assistant?
Yes, through a configured script or tool that calls your endpoint. The assistant keeps its own main model and reviews the result. An ordinary prompt alone does not install the integration.
How do I judge coding quality?
Use task completion and passing tests on your own repository. Check whether generated tests detect the original failure, not simply whether they pass after the change.
How much does running Qwen2.5-Coder-7B on AI Fabric cost?
Measure cost per accepted patch or useful test set. Include unsuccessful attempts and validation time when comparing this worker with another coding setup.
The GPU billing unit and idle policy are confirmed during onboarding. Managed Models API rates do not apply to this deployment.
Checkpoint: Qwen/Qwen2.5-Coder-7B-Instruct. Access requirements and licence: Qwen2.5-Coder-7B publisher reference.