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README.md

@@ -1,7 +1,94 @@
 ---
-language: en
+license: other
+language:
+  - en
 pipeline_tag: text-generation
 library_name: mlx
 tags:
-- mlx
+  - mlx
+  - svg
+  - code-generation
+  - graphics
+  - vector-graphics
+  - apple-silicon
+  - llama
 ---
+
+# SVG-Master
+
+**Subtitle**: Expert SVG generation from natural language.
+
+SVG-Master is a specialized fine-tuned LLM designed for high-quality **SVG code generation**. It takes natural language descriptions and outputs clean, valid, and optimized SVG code for icons, illustrations, and graphics.
+
+Built on top of **Llama 3.2 3B Instruct**, this model has been instruction-tuned specifically to understand geometry, color theory, and SVG syntax.
+
+## 🚀 Key Features
+
+- **🎨 Multi-element Support**: Generates complex scenes with shapes, paths, gradients, and text.
+- **📐 Proper Scaling**: Automatically includes `viewBox` and dimensions for responsive graphics.
+- **✨ Clean Syntax**: Optimized, minimal SVG code that is easy to read and edit.
+- **⚖️ Balanced Precision**: Fine-tuned using LoRA on high-quality vector datasets.
+
+## 📊 Model Specifications
+
+| Parameter              | Value                               |
+| ---------------------- | ----------------------------------- |
+| **Base Model**         | Llama-3.2-3B-Instruct-4bit          |
+| **Model Size**         | 3.2 Billion Parameters              |
+| **Fine-tuning Method** | LoRA (QLoRA)                        |
+| **LoRA Config**        | 16 layers, Rank 32, Alpha 64        |
+| **Training Data**      | 3,000 High-quality SVG/Prompt pairs |
+| **Training Steps**     | 2,000 Iterations                    |
+| **Optimizer**          | Adam (Learning Rate: 1e-4)          |
+| **Context Window**     | 128k Tokens (Llama 3.2 default)     |
+
+## 🛠 Usage
+
+### 1. Local Inference (MLX)
+
+Designed for Apple Silicon, use `mlx-lm` for the fastest experience.
+
+```bash
+# Using uv (recommended)
+uv run mlx_lm.generate --model ./base_weights --adapter-path ./adapters --prompt "A minimalist sunset over a calm ocean"
+
+# Using standard python
+python -m mlx_lm.generate --model ./base_weights --adapter-path ./adapters --prompt "A minimalist sunset over a calm ocean"
+```
+
+### 2. Ollama Integration
+
+You can run the dequantized/fused version of the model directly in Ollama.
+
+```bash
+# Pull the model from Ollama Hub
+ollama run fahidnasir/svg-master "Generate a blue glowing circuit board icon"
+```
+
+### 3. Python API
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("fahidnasir/SVG-Master")
+response = generate(model, tokenizer, prompt="A golden trophy icon", max_tokens=1024)
+print(response)
+```
+
+## 📂 Project Structure
+
+- `base_weights/`: Original Llama-3.2-3B-Instruct-4bit weights.
+- `adapters/`: Fine-tuned LoRA adapter weights and configuration (Rank 32).
+- `data/`: Cleaned and balanced training/validation datasets.
+- `scripts/`: Automation scripts for training, verification, and export.
+- `weight_fused/`: Fully fused, dequantized GGUF/Safetensors models (for export).
+
+## 📜 License
+
+This model inherits the [Llama 3.2 Community License Agreement](https://www.llama.com/llama3_2/license/).
+
+## 🔗 Links
+
+- **HuggingFace**: [fahidnasir/SVG-Master](https://huggingface.co/fahidnasir/SVG-Master)
+- **Kaggle**: [nfahid/svg-master](https://www.kaggle.com/models/nfahid/svg-master)
+- **Ollama**: [fahidnasir/svg-master](https://ollama.com/fahidnasir/svg-master)

config.json

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     "num_hidden_layers": 28,
     "num_key_value_heads": 8,
     "pretraining_tp": 1,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4
+    },
     "rms_norm_eps": 1e-05,
     "rope_scaling": {
         "factor": 32.0,

model.safetensors.index.json

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     }
 }

tokenizer_config.json

@@ -2050,6 +2050,7 @@
     }
   },
   "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|eot_id|>",
   "extra_special_tokens": {},