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