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README.md

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+---
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+- fine-tuned
+- lora
+- mlx
+---
+
+# Fine-tuned Phi-3 Mini Model
+
+This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct using LoRA (Low-Rank Adaptation) and MLX.
+
+## Model Details
+
+- **Base Model**: microsoft/Phi-3-mini-4k-instruct
+- **Fine-tuning Method**: LoRA with MLX
+- **Model Size**: 252.1 MB
+
+## Usage
+
+```python
+from mlx_lm import load, generate
+
+# Load the model
+model, tokenizer = load("didierlopes/phi-3-mini-4k-instruct-ft-on-my-blog")
+
+# Generate text
+prompt = "<|system|>\nYou are a helpful assistant.<|end|>\n<|user|>\nHello!<|end|>\n<|assistant|>"
+response = generate(model, tokenizer, prompt, max_tokens=100)
+print(response)
+```
+
+## Training
+
+This model was fine-tuned using the MLX framework with LoRA adapters. The training process involved:
+
+1. Data preprocessing and validation
+2. LoRA configuration and setup
+3. Fine-tuning with custom training loop
+4. Adapter fusion into base model
+
+## License
+
+This model is released under the MIT license.

adapter_config.json

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+{
+    "num_layers": 32,
+    "lora_layers": 32,
+    "lora_parameters": {
+        "rank": 16,
+        "scale": 20.0,
+        "dropout": 0.1,
+        "keys": [
+            "self_attn.q_proj",
+            "self_attn.k_proj",
+            "self_attn.v_proj",
+            "self_attn.o_proj"
+        ]
+    }
+}

adapters.safetensors

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