初始化项目，由ModelHub XC社区提供模型

Model: chanifrusydi/gemma-3-270m-lora-finetuned
Source: Original Platform

README.md

@@ -0,0 +1,136 @@
+---
+base_model: unsloth/gemma-3-270m-it
+library_name: transformers
+pipeline_tag: text-generation
+license: gemma
+tags:
+- gemma3
+- lora
+- sft
+- unsloth
+- gguf
+---
+
+# gemma-3-270m-lora-finetuned
+
+A fine-tuned Gemma 3 270M model with LoRA adapters, trained on instruction-following data.
+
+## Model Details
+
+- **Base Model:** unsloth/gemma-3-270m-it
+- **Model Type:** LoRA fine-tuned (merged)
+- **Language:** English (instruction-tuned)
+- **License:** Gemma License
+
+## Available Formats
+
+| Format | File | Use Case |
+|--------|------|----------|
+| Safetensors | `model.safetensors` | Transformers, vLLM |
+| GGUF Q8_0 | `gguf/gemma-3-270m-Q8_0.gguf` | llama.cpp, Ollama |
+| GGUF BF16 | `gguf/gemma-3-270m-auto.gguf` | llama.cpp (full precision) |
+
+## Uses
+
+### Direct Use
+- Instruction following
+- Conversational AI
+- Question answering
+- Text generation
+
+### Out-of-Scope Use
+- Medical or legal advice
+- Generating harmful content
+- Production use without evaluation
+
+## Training Details
+
+### Training Data
+- **Dataset:** mlabonne/FineTome-100k (full dataset)
+- **Samples:** ~100,000
+- **Data Processing:** Standardized format with Gemma 3 chat template
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Precision | bfloat16 |
+| Max Sequence Length | 1024 |
+| Per Device Batch Size | 2 |
+| Gradient Accumulation Steps | 4 |
+| Effective Batch Size | 8 |
+| Max Steps | 12,500 |
+| Learning Rate | 2e-4 |
+| Warmup Steps | 2 |
+| Optimizer | adamw_8bit |
+| Weight Decay | 0.001 |
+| LR Scheduler | linear |
+| Seed | 3407 |
+
+### LoRA Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank (r) | 128 |
+| LoRA Alpha | 128 |
+| LoRA Dropout | 0 |
+| Bias | none |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+
+### Training Stats
+- **Training Time:** ~98 minutes (1h 38m)
+- **Peak VRAM Usage:** 12.4 GB (77.9% of 16 GB)
+
+### Compute Infrastructure
+- **Hardware:** NVIDIA RTX 5070 Ti (16 GB VRAM)
+- **Framework:** Unsloth, Transformers, TRL
+
+## How to Get Started
+
+### Using Transformers (Safetensors)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
+
+messages = [{"role": "user", "content": "Hello, how are you?"}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+
+output = model.generate(inputs, max_new_tokens=64, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Using GGUF (llama.cpp / Ollama)
+```bash
+# Download GGUF
+wget https://huggingface.co/chanifrusydi/gemma-3-270m-lora-finetuned/resolve/main/gguf/gemma-3-270m-Q8_0.gguf
+
+# Run with llama.cpp
+./llama-cli -m gemma-3-270m-Q8_0.gguf -p "Hello, how are you?"
+
+# Or import to Ollama
+ollama create gemma3-finetuned -f Modelfile
+```
+
+## Citation
+
+```bibtex
+@misc{gemma-3-270m-lora-finetuned,
+  author = {chanifrusydi},
+  title = {gemma-3-270m-lora-finetuned},
+  publisher = {HuggingFace},
+  year = {2025}
+}
+```
+
+## Acknowledgments
+
+- [Unsloth](https://github.com/unslothai/unsloth) for the fast fine-tuning framework
+- [Google](https://huggingface.co/google) for the Gemma 3 base model
+- [mlabonne](https://huggingface.co/mlabonne) for the FineTome-100k dataset