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README.md

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+---
+base_model: swiss-ai/Apertus-8B-2509
+tags:
+- gguf
+- llama.cpp
+- quantized
+- trl
+- sft
+---
+
+# Apertus-8B-tulu-xlam-gguf
+
+This is a GGUF conversion of [Colby/Apertus-8B-tulu-xlam-sft](https://huggingface.co/Colby/Apertus-8B-tulu-xlam-sft), which is a LoRA fine-tuned version of [swiss-ai/Apertus-8B-2509](https://huggingface.co/swiss-ai/Apertus-8B-2509).
+
+## Model Details
+
+- **Base Model:** swiss-ai/Apertus-8B-2509
+- **Fine-tuned Model:** Colby/Apertus-8B-tulu-xlam-sft
+- **Training:** Supervised Fine-Tuning (SFT) with TRL
+- **Format:** GGUF (for llama.cpp, Ollama, LM Studio, etc.)
+
+## Available Quantizations
+
+| File | Quant | Size | Description | Use Case |
+|------|-------|------|-------------|----------|
+| Apertus-8B-tulu-xlam-sft-f16.gguf | F16 | ~1GB | Full precision | Best quality, slower |
+| Apertus-8B-tulu-xlam-sft-q8_0.gguf | Q8_0 | ~500MB | 8-bit | High quality |
+| Apertus-8B-tulu-xlam-sft-q5_k_m.gguf | Q5_K_M | ~350MB | 5-bit medium | Good quality, smaller |
+| Apertus-8B-tulu-xlam-sft-q4_k_m.gguf | Q4_K_M | ~300MB | 4-bit medium | Recommended - good balance |
+
+## Usage
+
+### With llama.cpp
+
+```bash
+# Download model
+huggingface-cli download Colby/Apertus-8B-tulu-xlam-gguf Apertus-8B-tulu-xlam-sft-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama-cli -m Apertus-8B-tulu-xlam-sft-q4_k_m.gguf -p "Your prompt here"
+```
+
+### With Ollama
+
+1. Create a `Modelfile`:
+```
+FROM ./Apertus-8B-tulu-xlam-sft-q4_k_m.gguf
+```
+
+2. Create the model:
+```bash
+ollama create my-model -f Modelfile
+ollama run my-model
+```
+
+### With LM Studio
+
+1. Download the `.gguf` file
+2. Import into LM Studio
+3. Start chatting!
+
+## License
+
+Inherits the license from the base model: swiss-ai/Apertus-8B-2509
+
+## Citation
+
+```bibtex
+@misc{Apertus_8B_tulu_xlam_gguf,
+  author = {Colby},
+  title = {Apertus-8B-tulu-xlam-gguf},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/Colby/Apertus-8B-tulu-xlam-gguf}
+}
+```
+
+---
+
+*Converted to GGUF format using llama.cpp*