codelion
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Llama-3.2-1B-Instruct-tool-calling-lora

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 ---
+base_model: meta-llama/Llama-3.2-1B-Instruct
+tags:
+- ellora
+- lora
+- tool-calling
+- function-calling
+- code-exploration
+- magpie
+- peft
+- llama
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+inference: true
+model_type: llama
 ---
+# codelion/Llama-3.2-1B-Instruct-tool-calling-lora
+## 🛠️ Tool Calling LoRA with Magpie
+This LoRA adapter enhances meta-llama/Llama-3.2-1B-Instruct with tool calling capabilities for code exploration and manipulation. Trained using a hybrid Magpie + real execution approach on diverse coding scenarios.
+## 🎯 Key Features
+- **Tool Calling**: Teaches models to use development tools effectively
+- **Code Exploration**: Navigate and understand unfamiliar codebases
+- **Real Execution**: Training data generated from actual tool execution
+- **OpenAI Format**: Compatible with OpenAI function calling format
+- **Multi-Tool Sequences**: Learns to chain multiple tools for complex tasks
+## 📊 Performance Metrics
+- **Base Model**: meta-llama/Llama-3.2-1B-Instruct
+- **Training Method**: Standard LoRA fine-tuning
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 128
+- **Training Samples**: 1000
+- **Sequence Success Rate**: 100.0%
+- **Tool Call Accuracy**: 100.0%
+- **Average Tools per Sequence**: 3.0
+## 🔧 Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load base model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.2-1B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+# Load tool calling LoRA adapter
+model = PeftModel.from_pretrained(model, "codelion/Llama-3.2-1B-Instruct-tool-calling-lora")
+# Example: Use with tool calling prompt
+prompt = '''You have access to the following tools:
+- list_directory: List contents of a directory
+- search_files: Search for files containing specific content
+- read_file: Read a single file's contents
+- get_file_info: Get file metadata
+User: Help me understand how user authentication works in this Flask application
+Response:
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## 📈 Expected Output Format
+The model will generate tool calling sequences in OpenAI format:
+```json
+{
+  "tool_calls": [
+    {
+      "id": "call_1",
+      "type": "function",
+      "function": {
+        "name": "search_files",
+        "arguments": "{\"query\": \"auth\", \"file_types\": [\".py\"]}"
+      }
+    },
+    {
+      "id": "call_2",
+      "type": "function",
+      "function": {
+        "name": "read_file",
+        "arguments": "{\"path\": \"app.py\"}"
+      }
+    }
+  ]
+}
+```
+## 🧪 Training Details
+- **Method**: Standard LoRA fine-tuning with tool calling data
+- **Data Generation**: Magpie scenarios + real tool execution
+- **Tool Execution**: Safe sandbox environment for code exploration
+- **Scenario Types**: Code exploration, bug hunting, feature addition, refactoring
+- **Quality Validation**: Minimum tool usage and success rate thresholds
+## 📚 Available Tools
+The model is trained to use these development tools:
+1. **list_directory**: Browse project structure
+   - Parameters: `path` (directory to list)
+2. **search_files**: Find files containing specific content
+   - Parameters: `query`, `path`, `file_types`, `regex`
+3. **read_file**: Read complete file contents
+   - Parameters: `path` (file to read)
+4. **read_multiple_files**: Read multiple files at once
+   - Parameters: `paths` (list of files)
+5. **get_file_info**: Get file metadata
+   - Parameters: `path` (file or directory)
+6. **create_file**: Create new files (if safety mode disabled)
+   - Parameters: `path`, `content`
+7. **edit_file**: Modify existing files (if safety mode disabled)
+   - Parameters: `path`, `changes`
+## 🎭 Tool Usage Patterns Learned
+- **Exploration First**: Start with `list_directory` to understand structure
+- **Search Before Read**: Use `search_files` to find relevant files
+- **Batch Operations**: Use `read_multiple_files` for related files
+- **Progressive Refinement**: Start broad, then focus on specific files
+## 🔬 Evaluation
+The adapter was evaluated on diverse coding scenarios:
+- Sequence success rate: 100.0%
+- Tool call accuracy: 100.0%
+- Average tools per sequence: 3.0
+- Successful executions: 5/5
+## 🏷️ Tool Usage Distribution
+Most frequently used tools during evaluation:
+- **list_directory**: 5 uses
+- **read_file**: 5 uses
+- **search_files**: 5 uses
+## 🏷️ Related
+- **Dataset**: [codelion/Llama-3.2-1B-Instruct-magpie-tool-calling](https://huggingface.co/datasets/codelion/Llama-3.2-1B-Instruct-magpie-tool-calling)
+- **Base Model**: [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+- **Framework**: [PEFT](https://github.com/huggingface/peft)
+- **Training Approach**: Magpie + Real Execution
+---
+*This adapter is part of the [Ellora project](https://github.com/codelion/ellora) - standardized recipes for enhancing LLM capabilities.*