Upload tool calling fine-tuned ONNX INT4 model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.onnx.data filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- qwen2.5
+- onnx
+- onnxruntime-genai
+- int4
+- tool-calling
+- local-llm
+- dotnet
+- elbruno
+- fine-tuned
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+model-index:
+- name: Qwen2.5-0.5B-LocalLLMs-ToolCalling
+  results: []
+---
+
+# Qwen2.5-0.5B-LocalLLMs-ToolCalling
+
+Fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) optimized for **tool calling** in [ElBruno.LocalLLMs](https://github.com/elbruno/ElBruno.LocalLLMs).
+
+> **No Python needed.** Download and use directly in .NET with ONNX Runtime GenAI.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Base Model** | Qwen/Qwen2.5-0.5B-Instruct |
+| **Fine-Tuning** | QLoRA (rank 16, alpha 32) |
+| **Training Data** | Tool calling + RAG + instruction following (5,000 examples) |
+| **Format** | ONNX INT4 (ONNX Runtime GenAI) |
+| **Size** | ~837 MB |
+| **Context Length** | 2,048 tokens |
+| **Parameters** | 0.5B |
+| **License** | Apache 2.0 |
+
+## Key Features
+
+✅ **No Python needed** — Download and use directly in .NET  
+✅ **Optimized for ElBruno.LocalLLMs** — Matches QwenFormatter ChatML template exactly  
+✅ **Better tool calling accuracy** — Improved `<tool_call>` JSON format compliance  
+✅ **RAG grounded answering** — Cites context sources accurately  
+✅ **Runs on CPU** — No GPU required (faster with GPU)  
+✅ **Tiny model** — 0.5B parameters fit on edge devices and laptops
+
+## Usage with ElBruno.LocalLLMs
+
+### Install the NuGet package
+
+```bash
+dotnet add package ElBruno.LocalLLMs
+```
+
+### C# Code Example
+
+```csharp
+using ElBruno.LocalLLMs;
+using Microsoft.Extensions.AI;
+
+// Configure the fine-tuned model
+var options = new LocalLLMsOptions
+{
+    Model = new ModelDefinition
+    {
+        Id = "Qwen2.5-0.5B-LocalLLMs-ToolCalling".ToLower(),
+        HuggingFaceRepoId = "elbruno/Qwen2.5-0.5B-LocalLLMs-ToolCalling",
+        RequiredFiles = ["*"],
+        ModelType = OnnxModelType.GenAI,
+        ChatTemplate = ChatTemplateFormat.Qwen,
+        SupportsToolCalling = true
+    }
+};
+
+// Create the chat client (downloads model automatically on first use)
+using var client = await LocalChatClient.CreateAsync(options);
+
+// --- Tool Calling Example ---
+var tools = new List<AITool>
+{
+    AIFunctionFactory.Create(
+        (string city) => $"{{\"temp\": 22, \"condition\": \"sunny\"}}",
+        "get_weather",
+        "Get current weather for a city"
+    )
+};
+
+var response = await client.GetResponseAsync(
+    new[] { new ChatMessage(ChatRole.User, "What's the weather in Paris?") },
+    new ChatOptions { Tools = tools }
+);
+Console.WriteLine(response);
+
+// --- RAG Example ---
+var ragMessages = new[]
+{
+    new ChatMessage(ChatRole.System, "Answer based on the provided context."),
+    new ChatMessage(ChatRole.User,
+        "Context:\n[1] ONNX Runtime GenAI enables local LLM inference.\n\n"
+        + "Question: What does ONNX Runtime GenAI do?")
+};
+var ragResponse = await client.GetResponseAsync(ragMessages);
+Console.WriteLine(ragResponse);
+```
+
+## Training Details
+
+### Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| **LoRA Rank** | 16 |
+| **LoRA Alpha** | 32 |
+| **LoRA Dropout** | 0.05 |
+| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| **Learning Rate** | 2e-4 |
+| **Epochs** | 3 |
+| **Batch Size** | 16 (effective: 4 × 4 gradient accumulation) |
+| **Optimizer** | paged_adamw_8bit |
+| **Scheduler** | Cosine with 50-step warmup |
+| **Max Sequence Length** | 2,048 |
+| **Precision** | FP16 (mixed precision training) |
+
+### Training Data
+
+The model was fine-tuned on a curated dataset of 5,000 examples:
+
+| Category | Count | Source |
+|----------|-------|--------|
+| Tool Calling | 2,000 | Glaive Function Calling v2 + custom ElBruno.LocalLLMs examples |
+| RAG Grounded | 1,500 | MS MARCO + custom library documentation Q&A |
+| Chat Template | 1,500 | Alpaca + ShareGPT (filtered, reformatted to ChatML) |
+
+All training data matches the exact format produced by `QwenFormatter.cs` — including `<tool_call>` tags, ChatML tokens (`<|im_start|>`, `<|im_end|>`), and tool result formatting.
+
+### Training Framework
+
+- **[Unsloth](https://github.com/unslothai/unsloth)** — 2x faster QLoRA training with 50% less VRAM
+- **[HuggingFace TRL](https://github.com/huggingface/trl)** — SFTTrainer for supervised fine-tuning
+- **Hardware:** NVIDIA RTX 4090 (24 GB VRAM) or equivalent
+
+## Benchmark Results
+
+<!-- Replace with actual benchmark results after evaluation -->
+
+| Metric | Base Model | Fine-Tuned | Improvement |
+|--------|-----------|-----------|-------------|
+| Tool Call Accuracy | — | — | — |
+| JSON Format Compliance | — | — | — |
+| RAG Citation Accuracy | — | — | — |
+| ChatML Adherence | — | — | — |
+| Inference Speed (tokens/sec) | — | — | — |
+
+*Benchmarks will be updated after comprehensive evaluation.*
+
+## ONNX Conversion Pipeline
+
+The model was converted using this pipeline:
+
+```
+Qwen2.5 Base → QLoRA Fine-tune → Merge LoRA → ONNX Export (INT4)
+```
+
+1. **Fine-tune** with QLoRA (Unsloth + TRL)
+2. **Merge** LoRA adapters into base model (`merge_lora.py`)
+3. **Convert** to ONNX with `onnxruntime_genai.models.builder` INT4 quantization (`convert_to_onnx.py`)
+4. **Validate** against QwenFormatter test suite (`validate_onnx.py`)
+5. **Upload** to HuggingFace (`upload_to_hf.py`)
+
+All scripts are available at: [`scripts/finetune/`](https://github.com/elbruno/ElBruno.LocalLLMs/tree/main/scripts/finetune)
+
+## Intended Use
+
+### Primary Use Cases
+
+- **Tool Calling** — Small model that reliably produces `<tool_call>` JSON for function execution
+- **RAG** — Grounded answering with source citations from provided context
+- **Local Inference** — Privacy-preserving AI on laptops, edge devices, and CI/CD pipelines
+- **.NET Applications** — Seamless integration via ElBruno.LocalLLMs NuGet package
+
+### Out of Scope
+
+- Complex multi-step reasoning (use 7B+ models)
+- Multilingual tasks (English-only training data)
+- Long-context tasks beyond 2,048 tokens
+- Safety-critical applications without additional guardrails
+
+## Limitations
+
+- **0.5B model** — Limited reasoning compared to larger models (3B, 7B, 14B)
+- **English only** — Not trained on multilingual data
+- **Simple tools** — Best with 1–3 tools per conversation; may struggle with 10+ complex tools
+- **INT4 quantization** — Slight quality degradation (~1-3%) compared to FP16, especially on edge cases
+- **No streaming tool calls** — Tool call output is generated as a complete block
+
+## Citation
+
+```bibtex
+@misc{{{MODEL_NAME.lower().replace('-', '_').replace('.', '_')}}},
+  author = {{Bruno Capuano}},
+  title = {Qwen2.5-0.5B-LocalLLMs-ToolCalling},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/elbruno/Qwen2.5-0.5B-LocalLLMs-ToolCalling}
+}
+```
+
+## Acknowledgments
+
+- **Base Model:** [Qwen Team](https://github.com/QwenLM/Qwen2.5) — Qwen2.5 family
+- **Training Framework:** [Unsloth](https://github.com/unslothai/unsloth) — Fast QLoRA training
+- **ONNX Conversion:** [ONNX Runtime GenAI](https://github.com/microsoft/onnxruntime-genai) — Microsoft
+- **Training Data:** [Glaive AI](https://huggingface.co/glaiveai) — Function calling dataset
+- **Library:** [ElBruno.LocalLLMs](https://github.com/elbruno/ElBruno.LocalLLMs) — .NET local LLM inference

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

genai_config.json

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+{
+    "model": {
+        "bos_token_id": 1,
+        "context_length": 32768,
+        "decoder": {
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            },
+            "filename": "model.onnx",
+            "head_size": 64,
+            "hidden_size": 896,
+            "inputs": {
+                "input_ids": "input_ids",
+                "attention_mask": "attention_mask",
+                "past_key_names": "past_key_values.%d.key",
+                "past_value_names": "past_key_values.%d.value"
+            },
+            "outputs": {
+                "logits": "logits",
+                "present_key_names": "present.%d.key",
+                "present_value_names": "present.%d.value"
+            },
+            "num_attention_heads": 14,
+            "num_hidden_layers": 24,
+            "num_key_value_heads": 2
+        },
+        "eos_token_id": 151645,
+        "pad_token_id": 151665,
+        "type": "qwen2",
+        "vocab_size": 151936
+    },
+    "search": {
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": true,
+        "length_penalty": 1.0,
+        "max_length": 32768,
+        "min_length": 0,
+        "no_repeat_ngram_size": 0,
+        "num_beams": 1,
+        "num_return_sequences": 1,
+        "past_present_share_buffer": true,
+        "repetition_penalty": 1.0,
+        "temperature": 1.0,
+        "top_k": 50,
+        "top_p": 1.0
+    }
+}

model.onnx

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+size 189195

model.onnx.data

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tokenizer.json

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+size 11422356

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": true,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}