student-abdullah/Quantized_Qwen-2.5-Coding-0.5B_icl_mlp

Uploaded model

• Developed by: student-abdullah
• License: apache-2.0
• Quantized from model: Qwen2.5-Coder-0.5B
• Created on: 06th July, 2025

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Acknowledgement

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Quantization Description

This model is quantized using selective quantization from the Qwen2.5-Coder-0.5B base model to increase its speed while preserving the capabilities in generating relevant and accurate responses related python programming. The quantization method included 32-bit quantization of the following Layers:

• q_proj
• k_proj
• v_proj
• o_proj
• down_proj
• gate_proj
• up_proj
• lm_head

Rest of the remaining layers were quantized to q3_k_l

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Model Description

Layer Name	Role (Short)	Type
q_proj, k_proj, v_proj	Compute query, key, and value for attention mechanism	Attention Proj
o_proj	Projects attention output back to model hidden size	Attention Proj
down_proj	Projects MLP output down to hidden size	MLP
gate_proj	First part of Gated MLP, controls info flow	MLP
up_proj	Expands hidden size in MLP	MLP
lm_head	Final linear layer for logits	Output Head
embed_tokens	Token embedding layer	Input Embed
norm	Final layernorm	Normalization
*_layernorm	Normalize inputs to layers	Normalization

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Model Architect

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)
)

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Performance & Limitations

• YET TO BE EXAMINED

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Model Performace Evaluation:

• YET TO BE EVALUATED