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Quantized_Qwen-2.5-Coding-0.5B_Fp16Q2_mixed_selective
student-abdullah/Quantized_Qwen-2.5-Coding-0.5B_Fp16Q2_mixed_selective
Quantized_Qwen-2.5-Coding-0.5B_Fp16Q2_mixed_selective is a machine learning model from student-abdullah. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
- Developed by: student-abdullah - License: apache-2.0 - Quantized from model: Qwen2.5-Coder-0.5B - Created on: 14th July, 2025
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jul 14, 2025
- Updated
- Jul 14, 2025
- SHA
- b910ec9e
Base models
Datasets
Model card
From the Hugging Face model README
Uploaded model
- Developed by: student-abdullah
- License: apache-2.0
- Quantized from model: Qwen2.5-Coder-0.5B
- Created on: 14th July, 2025
Acknowledgement
<div style="display: flex; gap: 10px; align-items: center;"> <img src="https://colab.research.google.com/img/colab_favicon_256px.png" width="200"/> <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ef/ChatGPT-Logo.svg/2048px-ChatGPT-Logo.svg.png" width="140"/> <img src="https://compareaimodels.com/content/images/2024/08/qwen-square.svg" width="200"/> </div>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 16-bit quantization of the following Layers:
- q_proj
- v_proj
- o_proj
- down_proj
- lm_head
Rest of the remaining layers were quantized to Q2
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
<pre><code>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>Performance & Limitations
- YET TO BE EXAMINED
Model Performace Evaluation:
- YET TO BE EVALUATED
- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Jul 14, 2025
- Updated
- Jul 14, 2025