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+WeDLM-8B-Instruct-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+WeDLM-8B-Instruct-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
 license: apache-2.0
+language:
+  - en
+  - zh
+base_model: tencent/WeDLM-8B-Instruct
+tags:
+  - gguf
+  - llama-cpp
+  - wedlm
+  - tencent
+  - qwen3
+  - quantized
+library_name: gguf
+pipeline_tag: text-generation
 ---
+
+# WeDLM-8B-Instruct-GGUF
+
+**First GGUF quantization of Tencent WeDLM-8B-Instruct!**
+
+Quantized using llama.cpp b7688.
+
+Original model: [tencent/WeDLM-8B-Instruct](https://huggingface.co/tencent/WeDLM-8B-Instruct)
+
+## About
+
+WeDLM is an 8B parameter instruction-tuned model by Tencent, supporting English and Chinese. It features QK Norm architecture similar to Qwen3.
+
+This GGUF uses `qwen3` architecture identifier for maximum llama.cpp compatibility.
+
+## Available Files
+
+| Filename | Quant | Size | Description |
+|----------|-------|------|-------------|
+| WeDLM-8B-Instruct-Q4_K_M.gguf | Q4_K_M | 4.68 GB | Good quality, recommended for most use cases |
+| WeDLM-8B-Instruct-Q8_0.gguf | Q8_0 | 8.11 GB | High quality, best accuracy |
+
+## Performance Benchmarks
+
+### CPU (16 threads, Zen4)
+
+| Quant | Prompt Processing | Text Generation |
+|-------|-------------------|-----------------|
+| Q4_K_M | 88.65 t/s | 8.27 t/s |
+| Q8_0 | 50.80 t/s | 5.17 t/s |
+
+### GPU (RTX 4060 Laptop, 8GB VRAM)
+
+| Quant | Prompt Processing | Text Generation |
+|-------|-------------------|-----------------|
+| Q4_K_M | **1833.84 t/s** | **37.08 t/s** |
+
+*Q4_K_M recommended for RTX 4060 (fits in 8GB VRAM)*
+
+## Prompt Format (ChatML)
+
+```
+<|im_start|>system
+You are a helpful AI assistant.<|im_end|>
+<|im_start|>user
+Hello!<|im_end|>
+<|im_start|>assistant
+```
+
+## Usage
+
+### llama.cpp
+
+```bash
+./llama-cli -m WeDLM-8B-Instruct-Q4_K_M.gguf \
+  -p "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n" \
+  -n 256 -ngl 99
+```
+
+### Ollama
+
+```bash
+# Create Modelfile
+cat > Modelfile << 'EOF'
+FROM ./WeDLM-8B-Instruct-Q4_K_M.gguf
+TEMPLATE "<|im_start|>user\n{{ .Prompt }}<|im_end|>\n<|im_start|>assistant\n"
+EOF
+
+ollama create wedlm -f Modelfile
+ollama run wedlm
+```
+
+## Hardware Requirements
+
+| Quant | Min VRAM | Recommended RAM |
+|-------|----------|-----------------|
+| Q4_K_M | 6 GB | 8 GB |
+| Q8_0 | 10 GB | 12 GB |
+
+## Model Architecture
+
+- Parameters: 8.19B
+- Layers: 36
+- Hidden Size: 4096
+- Attention Heads: 32 (8 KV heads, GQA)
+- Context Length: 16384
+- Features: QK Norm, SwiGLU, RoPE (theta=1M)
+
+## Acknowledgements
+
+- Original model: [Tencent WeDLM Team](https://huggingface.co/tencent)
+- Inference framework: [llama.cpp](https://github.com/ggml-org/llama.cpp)
+
+## Disclaimer
+
+This is an unofficial quantization. For official support, please refer to the original model repository.

WeDLM-8B-Instruct-Q4_K_M.gguf

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WeDLM-8B-Instruct-Q8_0.gguf

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