qwen2-7b-instruct

Paper: Qwen2 Technical Report (arXiv:2407.10671) · HF Papers

See our collection for all Qwen2 versions.

Run Qwen2 with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/qwen2-7b-instruct

Qwen2 is Alibaba's decoder-only transformer family: grouped-query attention with q/k/v bias, SwiGLU MLPs, RMSNorm, and rotary positions, in dense 0.5B-72B sizes (plus the Qwen2-57B-A14B mixture-of-experts), as base and instruct variants.

For more details on the model, please see the upstream model card.

Pure-Keras 3 conversion of Qwen/Qwen2-7B-Instruct for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an instruct (chat-tuned) checkpoint; load Qwen2Tokenizer so the chat template is applied.

Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.qwen2 import Qwen2TextGenerate, Qwen2Tokenizer

model = Qwen2TextGenerate.from_weights("zeromodels/qwen2-7b-instruct")
tokenizer = Qwen2Tokenizer.from_weights("zeromodels/qwen2-7b-instruct")

inputs = tokenizer([
    {"role": "user", "content": "Explain rotary embeddings in one sentence."}
])
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))

Load any Qwen2 variant the same way with from_weights("zeromodels/<variant>"):

Tips

• Set KERAS_BACKEND before importing Keras / zeromodels.
• Prefer Qwen2Tokenizer.from_weights(...) so the chat template matches.
• Larger checkpoints: try load_dtype="bfloat16" or quantization="int8".
• See Loading Weights and the Qwen2 docs.
• Community / upstream safetensors still work via the hf: prefix, e.g. Qwen2TextGenerate.from_weights("hf:Qwen/Qwen2-7B-Instruct").

Special Thanks

A huge thank you to the Qwen team at Alibaba for creating and releasing these models.

License: Apache 2.0.