metadata

base_model: Qwen/Qwen2.5-Coder-0.5B
datasets: None
language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - torch
  - trl
  - unsloth
  - llama
  - gguf

Uploaded model

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

Acknowledgement

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
v_proj
o_proj
down_proj
lm_head

Rest of the remaining layers were quantized to q3_k_l

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

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)
)

Performance & Limitations

YET TO BE EXAMINED

Model Performace Evaluation:

YET TO BE EVALUATED