Add Neuron-optimized files for Qwen/Qwen2.5-0.5B
Browse files🤖 Neuron Export Bot: Adding AWS Neuron-optimized model files.
Original model: [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B)
Task: text-generation
Generated by: [badaoui](https://huggingface.co/badaoui)
Generated using: [Optimum Neuron Compiler Space](https://huggingface.co/spaces/optimum/neuron-export)
These files have been pre-compiled for AWS Neuron devices (Inferentia/Trainium) and should provide improved inference performance.
- README.md +38 -0
- config.json +56 -0
- model.pt +3 -0
- neuron_config.json +27 -0
README.md
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---
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tags:
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- neuron
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- optimized
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- aws-neuron
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- text-generation
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base_model: Qwen/Qwen2.5-0.5B
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---
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# Neuron-Optimized Qwen/Qwen2.5-0.5B
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This repository contains AWS Neuron-optimized files for [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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## Model Details
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- **Base Model**: [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B)
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- **Task**: text-generation
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- **Optimization**: AWS Neuron compilation
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- **Generated by**: [badaoui](https://huggingface.co/badaoui)
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- **Generated using**: [Optimum Neuron Compiler Space](https://huggingface.co/spaces/optimum/neuron-export)
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## Usage
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This model has been optimized for AWS Neuron devices (Inferentia/Trainium). To use it:
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```python
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from optimum.neuron import NeuronModelForCausalLM
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model = NeuronModelForCausalLM.from_pretrained("badaoui/Qwen-Qwen2.5-0.5B-neuron")
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```
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## Performance
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These files are pre-compiled for AWS Neuron devices and should provide improved inference performance compared to the original model when deployed on Inferentia or Trainium instances.
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## Original Model
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For the original model, training details, and more information, please visit: [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B)
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 24,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.55.4",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:487556227641bd714fd53ffee225046b1d5aa399802ec8255a198260870af261
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size 3770117
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neuron_config.json
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{
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"_serialized_key": "NxDNeuronConfig",
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"batch_size": 1,
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"capacity_factor": null,
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"checkpoint_id": "Qwen/Qwen2.5-0.5B",
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"checkpoint_revision": "060db6499f32faf8b98477b0a26969ef7d8b9987",
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"continuous_batching": false,
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"ep_degree": 1,
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"fused_qkv": false,
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"glu_mlp": true,
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"local_ranks_size": 4,
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"max_batch_size": 1,
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"max_context_length": 128,
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"max_topk": 256,
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"n_active_tokens": 128,
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"neuronxcc_version": "2.21.18209.0+043b1bf7",
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"on_device_sampling": true,
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"optimum_neuron_version": "0.4.1",
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"output_logits": false,
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"pp_degree": 1,
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"sequence_length": 128,
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"speculation_length": 0,
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"start_rank_id": 0,
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"target": "inf2",
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"torch_dtype": "bfloat16",
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"tp_degree": 4
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}
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