Update README and integration test for Qwen2-7B-Instruct

[sdeeptan-aws]

Description

Updated Qwen2-7B-Instruct contrib model README and integration test. The modeling code is unchanged — Qwen2 is architecturally close to LLaMA (reuses NeuronLlamaMLP, standard RoPE, pre-norm decoder layers) with QKV bias enabled and tied embeddings. Validation achieves 100% token match.

Model Information

Model Name: Qwen2-7B-Instruct
Model Architecture: Decoder-only transformer (28 layers, hidden_size=3584, GQA 28Q heads, QKV bias, tied embeddings)
Purpose: Text generation / instruction following

Checklist

Required Components

• Accuracy Test (test/integration/test_model.py)
  • Token match accuracy validation
  • Test can compile and run the model on Neuron
• README.md with the following sections:
  • Usage Example: Clear code example showing how to use the model
  • Compatibility Matrix: Table showing tested Neuron SDK versions and instance types
  • Example Checkpoints: Links to compatible model checkpoints
  • Testing Instructions: Command to run the test suite for the model
• Source Code (src/)
  • Modeling code following NxD Inference patterns (unchanged in this PR)

Optional Components

• Unit Tests (CPU or Neuron-based)

Folder Structure

/contrib/models/Qwen2-7B-Instruct/
  README.md
  /src
    modeling_qwen2.py
  /test
    /integration
      test_model.py

Testing

Model was compiled and tested with TP=2, batch_size=1, seq_len=128, bfloat16.

Test Results:

Test	Status	Result
Smoke Test	✅ PASS	Model loads successfully
Token Matching	✅ PASS	100% match

Test Prompt: "def fibonacci(n):"

Compatibility

Tested with:

• Instance Type(s): Trn1
• Configuration: TP=2, batch_size=1, seq_len=128, bfloat16

Additional Information

• LLaMA-like architecture: Qwen2 reuses NeuronLlamaMLP directly. Standard RoPE, pre-norm decoder layers, SwiGLU activation.
• QKV bias: qkv_bias=True, o_bias=False — unlike LLaMA which has no bias on any attention projections.
• Tied embeddings: embed_tokens.weight cloned to lm_head.weight via update_state_dict_for_tied_weights.
• Fused QKV support: Optional fused QKV path concatenates Q/K/V weights into single Wqkv tensor when neuron_config.fused_qkv=True.

Related Issues

N/A

vLLM Integration

• This model/feature is intended for use with vLLM
• Documentation includes vLLM registration instructions

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By submitting this PR, I confirm that:

• I have read and followed the contributing guidelines
• This is a community contribution and may have limited testing compared to officially-supported models
• The code follows best practices and is well-documented
• All required components listed above are included

[aws-yishanm]

Approved because Readme and test were present.