Add Qwen DevOps Foundation LoRA adapter - 4xL40S trained

- Fine-tuned Qwen3-8B on DevOps datasets using LoRA
- Trained on 4x NVIDIA L40S GPUs for ~3 hours
- Supports Docker, Kubernetes, CI/CD, and SRE tasks
- 174MB LoRA adapter + tokenizer files
- Ready for inference with base Qwen/Qwen3-8B model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,74 @@
----
-license: apache-2.0
----
+# Qwen DevOps Foundation Model - LoRA Adapter
+
+This is a LoRA (Low-Rank Adaptation) adapter for the Qwen3-8B model, fine-tuned on DevOps-related datasets.
+
+## 🎯 Model Details
+
+- **Base Model**: `Qwen/Qwen3-8B`
+- **Training Method**: LoRA fine-tuning
+- **Hardware**: 4x NVIDIA L40S GPUs  
+- **Training Checkpoint**: 400
+- **Training Date**: 2025-08-07
+- **Training Duration**: ~3 hours
+
+## 🚀 Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-8B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/YOUR_REPO_NAME")
+
+# Use the model
+prompt = "How do I deploy a Kubernetes cluster?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## 📊 Training Data
+
+This model was trained on DevOps-related datasets including:
+- Stack Overflow DevOps questions and answers
+- Docker commands and configurations  
+- Kubernetes deployment guides
+- Infrastructure as Code examples
+- SRE incident response procedures
+- CI/CD pipeline configurations
+
+## 🔧 Model Architecture
+
+- **LoRA Rank**: 16
+- **LoRA Alpha**: 32  
+- **Target Modules**: All linear layers
+- **Trainable Parameters**: ~43M (0.53% of base model)
+
+## 📋 Files Included
+
+- `adapter_model.safetensors`: LoRA adapter weights (main model file)
+- `adapter_config.json`: LoRA configuration parameters
+- `tokenizer.json`: Fast tokenizer configuration
+- `tokenizer_config.json`: Tokenizer settings and parameters
+- `special_tokens_map.json`: Special token mappings
+- `vocab.json`: Vocabulary mapping
+- `merges.txt`: BPE merge rules
+
+## 📄 License
+
+Apache 2.0
+
+## 🙏 Acknowledgments
+
+- Base model: [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) by Alibaba Cloud
+- Training infrastructure: HuggingFace Spaces (4x L40S GPUs)
+- Training framework: Transformers + PEFT

adapter_config.json

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+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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special_tokens_map.json

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+}

tokenizer.json

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