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Model: justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.npy filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+tags:
+  - smollm2
+  - onnx
+  - transformers.js
+  - text-generation
+  - fine-tuned
+  - sft
+  - dpo
+  - lora
+  - resume
+  - chatbot
+base_model: "HuggingFaceTB/SmolLM2-360M-Instruct"
+datasets:
+  - "justinthelaw/Resume-DPO-SFT-Dataset"
+pipeline_tag: text-generation
+---
+
+# justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO
+
+A fine-tuned version of [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) trained with a two-stage pipeline (SFT + DPO) to answer questions about **Justin**'s professional background, skills, and experience.
+
+## Model Description
+
+This model is designed for browser-based inference using [transformers.js](https://huggingface.co/docs/transformers.js). It powers a personal website chatbot that can answer questions about Justin's resume, work experience, education, and skills.
+
+### Training Pipeline
+
+The model is trained using a two-stage approach optimized for factual memorization:
+
+1. **SFT (Supervised Fine-Tuning)**: Primary training for factual memorization using conversation-formatted QA pairs
+2. **DPO (Direct Preference Optimization)**: Refinement training to prefer accurate answers over hallucinations
+
+### Training Details
+
+- **Base Model**: [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
+- **Training Dataset**: [justinthelaw/Resume-DPO-SFT-Dataset](https://huggingface.co/datasets/justinthelaw/Resume-DPO-SFT-Dataset)
+- **LoRA Configuration**:
+  - Rank (r): 32
+  - Alpha: 64
+  - Dropout: 0.05
+  - Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+
+#### SFT Training Configuration
+
+- Epochs: 5
+- Batch Size: 8
+- Learning Rate: 0.0001
+
+#### DPO Training Configuration
+
+- Epochs: 1
+- Batch Size: 4
+- Learning Rate: 5e-06
+- Beta: 0.05
+- Loss Type: sigmoid
+
+## Model Formats
+
+This repository contains multiple model formats:
+
+| Format         | Location                     | Use Case                            |
+| -------------- | ---------------------------- | ----------------------------------- |
+| SafeTensors    | `/` (root)                   | Python/PyTorch inference            |
+| ONNX           | `/onnx/model.onnx`           | Full precision ONNX Runtime         |
+| ONNX Quantized | `/onnx/model_quantized.onnx` | Browser inference (transformers.js) |
+
+> **Note**: If quantization fails during export due to weight distribution issues, `model_quantized.onnx` will be a copy of the fp16 model for compatibility.
+
+## Usage
+
+### Browser (transformers.js)
+
+```javascript
+import { pipeline } from "@huggingface/transformers";
+
+const generator = await pipeline(
+  "text-generation",
+  "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO",
+  { dtype: "q8" } // Uses model_quantized.onnx
+);
+
+const output = await generator("What is Justin's background?", {
+  max_new_tokens: 256,
+  temperature: 0.7,
+});
+```
+
+### Python (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
+tokenizer = AutoTokenizer.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
+
+prompt = "What is Justin's background?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Intended Use
+
+This model is intended for:
+
+- Personal website chatbots
+- Resume Q&A applications
+- Demonstrating fine-tuning techniques for personalized AI assistants
+
+## Limitations
+
+- The model is specifically trained on Justin's resume and may not generalize to other topics
+- Responses are based on training data and may not reflect real-time information
+- Not suitable for general-purpose question answering
+
+## Author
+
+### Justin
+
+- GitHub: [justinthelaw](https://github.com/justinthelaw)
+- HuggingFace: [justinthelaw](https://huggingface.co/justinthelaw)
+
+## License
+
+This model is released under the Apache 2.0 license.

chat_template.jinja

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+{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "mlp_bias": false,
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+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "4.55.4",
+  "use_cache": true,
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+}

generation_config.json

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+  "pad_token_id": 2,
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+}

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onnx/chat_template.jinja

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+{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

onnx/config.json

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onnx/generation_config.json

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+}