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README.md
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@@ -54,33 +54,7 @@ Rest of the remaining layers were quantized to *q3_k_l*
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---
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# Model Architect
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Qwen2ForCausalLM(
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(model): Qwen2Model(
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(embed_tokens): Embedding(151936, 896, padding_idx=151665)
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(layers): ModuleList(
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(0-23): 24 x Qwen2DecoderLayer(
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(self_attn): Qwen2Attention(
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(q_proj): Linear(in_features=896, out_features=896, bias=True)
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(k_proj): Linear(in_features=896, out_features=128, bias=True)
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(v_proj): Linear(in_features=896, out_features=128, bias=True)
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(o_proj): Linear(in_features=896, out_features=896, bias=False)
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(rotary_emb): LlamaRotaryEmbedding()
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)
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(mlp): Qwen2MLP(
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(gate_proj): Linear(in_features=896, out_features=4864, bias=False)
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(up_proj): Linear(in_features=896, out_features=4864, bias=False)
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(down_proj): Linear(in_features=4864, out_features=896, bias=False)
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(act_fn): SiLU()
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)
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(input_layernorm): Qwen2RMSNorm((896,), eps=1e-06)
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(post_attention_layernorm): Qwen2RMSNorm((896,), eps=1e-06)
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)
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)
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(norm): Qwen2RMSNorm((896,), eps=1e-06)
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(rotary_emb): LlamaRotaryEmbedding()
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)
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(lm_head): Linear(in_features=896, out_features=151936, bias=False)
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)
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---
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# Performance & Limitations
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---
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# Model Architect
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<pre><code>```python Qwen2ForCausalLM( (model): Qwen2Model( (embed_tokens): Embedding(151936, 896, padding_idx=151665) (layers): ModuleList( (0-23): 24 x Qwen2DecoderLayer( (self_attn): Qwen2Attention( (q_proj): Linear(in_features=896, out_features=896, bias=True) (k_proj): Linear(in_features=896, out_features=128, bias=True) (v_proj): Linear(in_features=896, out_features=128, bias=True) (o_proj): Linear(in_features=896, out_features=896, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): Qwen2MLP( (gate_proj): Linear(in_features=896, out_features=4864, bias=False) (up_proj): Linear(in_features=896, out_features=4864, bias=False) (down_proj): Linear(in_features=4864, out_features=896, bias=False) (act_fn): SiLU() ) (input_layernorm): Qwen2RMSNorm((896,), eps=1e-06) (post_attention_layernorm): Qwen2RMSNorm((896,), eps=1e-06) ) ) (norm): Qwen2RMSNorm((896,), eps=1e-06) (rotary_emb): LlamaRotaryEmbedding() ) (lm_head): Linear(in_features=896, out_features=151936, bias=False) ) ```</code></pre>
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---
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# Performance & Limitations
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