Qwen Guide 2025: Alibaba's Best Open-Source LLM

What Is Qwen?

Qwen (pronounced "Chwen") is Alibaba Cloud's series of open-source large language models, developed by the Tongyi Lab team. The Qwen2.5 generation, released in late 2024, represents a major leap — offering models trained on 18 trillion tokens with 128K context length, strong instruction-following, and best-in-class multilingual support spanning 29+ languages.

Most Qwen2.5 variants are released under the MIT license, making them freely usable for commercial applications. Models above 72B (such as the 110B MoE variant) use a custom Qianwen License.

The four main product lines in the Qwen2.5 generation:

• Qwen2.5 — General-purpose text models (0.5B to 72B). Drop-in replacements for Llama with better multilingual and math performance.
• Qwen2.5-Coder — Coding specialist (1.5B to 32B). Qwen2.5-Coder-32B-Instruct rivals GPT-4o on HumanEval and is the top open coding model as of 2025.
• Qwen2.5-VL — Vision-language models for image understanding and document parsing.
• QwQ-32B — Reasoning model trained with chain-of-thought RL, achieving o1-class performance on MATH, AIME, and GPQA.

Model Lineup & Size Guide

Choose the right Qwen2.5 model for your hardware and use case:

For resource-constrained environments, 4-bit quantized (AWQ/GPTQ) variants are available on HuggingFace and halve the VRAM requirements.

Run Locally with Ollama

Ollama is the fastest way to get a Qwen2.5 model running on your machine — no Python setup required. Ollama ships quantized GGUF models that run on CPU+RAM or GPU:

# General-purpose 7B (requires ~8 GB RAM/VRAM)
ollama run qwen2.5:7b

# Larger general model (requires ~20 GB)
ollama run qwen2.5:32b

# Coding specialist — top open coding model
ollama run qwen2.5-coder:32b

# Reasoning model (o1-class)
ollama run qwq

# List available Qwen tags
ollama list | grep qwen

After pulling, Ollama exposes a local OpenAI-compatible endpoint at http://localhost:11434/v1. Point any OpenAI SDK client at it by setting base_url.

Python with HuggingFace Transformers

Load Qwen2.5 directly from HuggingFace Hub with the transformers library. Qwen2.5 uses a standard apply_chat_template interface:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Qwen/Qwen2.5-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain the difference between RAG and fine-tuning."},
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        do_sample=True,
    )

generated = output_ids[0][len(inputs.input_ids[0]):]
print(tokenizer.decode(generated, skip_special_tokens=True))

pip install transformers accelerate torch

For Qwen2.5-Coder, swap the model name to Qwen/Qwen2.5-Coder-32B-Instruct. For QwQ-32B use Qwen/QwQ-32B.

Via API Services (Together AI, Groq, Fireworks)

No GPU? Access Qwen2.5 via inference API providers — all expose an OpenAI-compatible endpoint:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="YOUR_TOGETHER_API_KEY",
)

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-72B-Instruct-Turbo",
    messages=[
        {"role": "user", "content": "Write a Python merge sort implementation."}
    ],
    max_tokens=1024,
)
print(response.choices[0].message.content)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="YOUR_FIREWORKS_API_KEY",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/qwen2p5-coder-32b-instruct",
    messages=[
        {"role": "user", "content": "Review this code for bugs:\n\ndef fib(n):\n  if n == 0: return 1\n  return fib(n-1) + fib(n-2)"}
    ],
)
print(response.choices[0].message.content)

Provider comparison:

• Together AI — Qwen2.5-72B-Instruct-Turbo, QwQ-32B. Competitive pricing, high throughput.
• Fireworks AI — Qwen2.5-Coder-32B, Qwen2.5-72B. Fast inference, serverless.
• Groq — Qwen2.5-72B via GroqCloud. Extremely low latency (custom LPU hardware).

Production Serving with vLLM

For self-hosted production deployments, vLLM provides the best throughput via PagedAttention and continuous batching. Qwen2.5 models are fully supported:

pip install vllm

# Serve 7B on a single GPU
vllm serve Qwen/Qwen2.5-7B-Instruct \
  --host 0.0.0.0 \
  --port 8000

# Serve 72B across 4 GPUs (tensor parallelism)
vllm serve Qwen/Qwen2.5-72B-Instruct \
  --tensor-parallel-size 4 \
  --max-model-len 32768

# Serve Coder-32B with AWQ quantization (halves VRAM)
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
  --quantization awq \
  --tensor-parallel-size 2

# Serve QwQ-32B reasoning model
vllm serve Qwen/QwQ-32B \
  --tensor-parallel-size 2 \
  --max-model-len 32768

The vLLM server exposes an OpenAI-compatible API at /v1/chat/completions — use the same client code as any OpenAI integration. See the vLLM guide for full configuration options.

Qwen2.5 vs Llama 3.1 vs DeepSeek vs Mistral

How Qwen2.5 stacks up against the top open-source alternatives on key benchmarks:

QwQ-32B's MATH score of 92.7% rivals OpenAI o1-mini — a 32B model outperforming models 2x its size on mathematical reasoning.

Best Use Cases for Qwen Models

Qwen2.5's design choices make it the best open model for specific scenarios:

• Multilingual applications (Chinese + English) — Qwen2.5 was trained with a much higher proportion of Chinese-language data than Llama or Mistral. It is the default recommendation for any product targeting Chinese-speaking users or requiring high-quality Chinese-English translation.
• Coding assistants (Qwen2.5-Coder) — Qwen2.5-Coder-32B-Instruct is the top-ranked open coding model on HumanEval, LiveCodeBench, and BigCodeBench. Use it as a self-hosted backend for code completion, review, and generation.
• Long-context document processing — Native 128K context window across the entire Qwen2.5 line (compared to 8K on older open models). Suitable for RAG pipelines, legal document review, and long-form summarization without chunking.
• Mathematical & scientific reasoning (QwQ-32B) — QwQ-32B is the best open model for step-by-step math, physics problems, and analytical reasoning tasks. It "thinks" via chain-of-thought before answering, similar to OpenAI o1.
• Vision tasks (Qwen2.5-VL) — Qwen2.5-VL handles image description, chart reading, and document OCR. Strong performance on document understanding benchmarks, useful for invoice processing and visual QA.