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Qwen2.5-7B - AI Language Models Tool

Overview

Qwen2.5-7B is the 7.6B-parameter member of the Qwen2.5 family — an open-weight, decoder-only large language model series released by the Qwen team (Alibaba) as part of the Qwen2.5 launch on September 19, 2024. Qwen2.5 focuses on stronger instruction-following, improved coding and mathematical reasoning, structured-output generation (JSON), and much longer context windows than prior releases. The project provides both base and instruction-tuned checkpoints and specialized expert variants (Coder, Math), and the code and model cards are published on Hugging Face and the Qwen project blog. ([qwenlm.github.io](https://qwenlm.github.io/blog/qwen2.5/)) Designed for production NLP tasks that require large context windows and multi-domain capability, Qwen2.5-7B supports very long context lengths (model config reported at 131,072 tokens) and an instruction-tuned generation window (documented generation up to ~8K tokens for instruct variants). The model is released under an Apache-2.0 compatible license and is widely distributed via Hugging Face, with significant community adoption and downloads. The Qwen project also documents deployment recipes (transformers, vLLM, Ollama) and quantization options for lower-cost inference. ([huggingface.co](https://huggingface.co/Qwen/Qwen2.5-7B))

Model Statistics

  • Downloads: 917,526
  • Likes: 253
  • Pipeline: text-generation
  • Parameters: 7.6B

License: apache-2.0

Model Details

Architecture and training: Qwen2.5-7B is a dense, decoder-only transformer using RoPE position encodings, SwiGLU activations, RMSNorm, and attention QKV bias. The publicly published model card lists ~7.61B parameters (6.53B non-embedding), 28 transformer layers, and a GQA-style attention configuration (28 query heads / 4 KV heads). Context length in the checkpoints is set to 131,072 tokens; instruction-tuned variants document generation capacity up to 8,192 tokens (with recommended tooling and YaRN rope-scaling for extreme extrapolation). ([huggingface.co](https://huggingface.co/Qwen/Qwen2.5-7B)) Specializations, deployments and quantization: The Qwen2.5 family includes specialized Coder and Math variants trained on extensive curated corpora (for example, Qwen2.5-Coder was trained on large amounts of code-related tokens). The project provides guidance and community examples for deployment (Hugging Face Transformers, vLLM, OpenLLM, Ollama) and for quantized inference (AWQ/GPTQ/4-bit AWQ checkpoints are available or referenced on related model pages). For long-text use-cases, Qwen documentation recommends vLLM and describes config/rope_scaling (YaRN) workflows to enable longer-context inference. ([qwenlm.github.io](https://qwenlm.github.io/blog/qwen2.5/))

Key Features

  • Very large context configuration (131,072-token config; long-context tooling for extrapolation).
  • Instruction-tuned variant supports long-generation (documented generation up to ~8,192 tokens).
  • Architecture: RoPE, SwiGLU activations, RMSNorm, and attention QKV bias for efficient decoding.
  • Specialized expert variants: Qwen2.5-Coder (code-focused) and Qwen2.5-Math (math reasoning).
  • Multiple deployment paths: Hugging Face Transformers, vLLM (recommended), Ollama, OpenLLM; quantized GGUF/AWQ options.

Example Usage

Example (python):

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Example: load the instruction-tuned variant (uses HF model card recommendations)
model_name = "Qwen/Qwen2.5-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "Explain the difference between precision and recall in one paragraph."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

# generate (example max_new_tokens; adjust for your use)
outputs = model.generate(**inputs, max_new_tokens=256)
# strip prompt from generated ids
generated = tokenizer.batch_decode([out[inputs['input_ids'].shape[1]:] for out in outputs], skip_special_tokens=True)[0]
print(generated)

# Notes: Qwen docs recommend using vLLM for large-scale long-context deployments
# and YaRN rope_scaling for extreme-length extrapolation. See model card for details.
# Source: Hugging Face model card and Qwen2.5 blog. ([huggingface.co](https://huggingface.co/Qwen/Qwen2.5-7B))

Pricing

Weights and checkpoints for Qwen2.5-7B are published open-source (Apache-2.0) and are free to download from Hugging Face. Managed/hosted Qwen services (Qwen-Plus, Qwen-Turbo, and other specialized Qwen API endpoints) are offered commercially through Alibaba Cloud Model Studio and third-party API providers; pricing is usage-based (per-million-token tiers) and varies by model, context window, and region (see Alibaba Cloud Model Studio pages for specific model pricing examples such as qwen-math and qwen-doc-turbo). For the open-source HF checkpoint itself there is no provider price to download, but running inference (cloud or self-hosted) will incur compute costs determined by your deployment. ([huggingface.co](https://huggingface.co/Qwen/Qwen2.5-7B))

Benchmarks

Parameters: ≈7.61 billion (Source: https://huggingface.co/Qwen/Qwen2.5-7B)

Context length (config): 131,072 tokens (Source: https://huggingface.co/Qwen/Qwen2.5-7B)

Instruction-tuned generation (documented): up to 8,192 tokens (generation window for instruct variants) (Source: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)

Layers: 28 transformer layers (Source: https://huggingface.co/Qwen/Qwen2.5-7B)

Downloads (last month, Hugging Face model page): 917,526 (downloads last month shown on HF model page) (Source: https://huggingface.co/Qwen/Qwen2.5-7B)

Reported benchmark claims (from Qwen team): MMLU: 85+, HumanEval: 85+, MATH: 80+ (reported for Qwen2.5 family) (Source: https://qwenlm.github.io/blog/qwen2.5/)

Last Refreshed: 2026-01-16

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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