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Model: Harish241412/qwen2.5-1.5b-toolcalling-dpo Source: Original Platform
2026-08-29 19:28:17 +08:00
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--- a/README.md
+++ b/README.md
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+---
+library_name: transformers
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwen2.5
+- tool-calling
+- function-calling
+- dpo
+- direct-preference-optimization
+- preference-tuning
+- when2call
+- transformers
+- pytorch
+license: apache-2.0
+datasets:
+- nvidia/When2Call
+---
+
+# Qwen2.5-1.5B Tool Calling DPO
+
+A DPO fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), trained on NVIDIA's [When2Call](https://huggingface.co/datasets/nvidia/When2Call) preference dataset to improve tool-calling decision making.
+
+The model is trained to better distinguish between requests that **require a tool call**, requests that can be answered directly, and requests that cannot be answered using the available tools.
+
+> This model is based on Qwen2.5-1.5B-Instruct, which is licensed under Apache 2.0. The model was fine-tuned using NVIDIA's When2Call dataset, which is licensed under CC BY 4.0. The dataset license and attribution requirements apply to the use of the When2Call dataset.
+
+
+## Model Details
+
+| Property         | Value                       |
+| ---------------- | --------------------------- |
+| Base model       | Qwen/Qwen2.5-1.5B-Instruct  |
+| Parameters       | 1.5B                        |
+| Fine-tuning      | LoRA + DPO                  |
+| Dataset          | NVIDIA When2Call            |
+| Training split   | `train_pref`                |
+| Final checkpoint | Step 1000                   |
+| Model format     | Merged                      |
+| Framework        | PyTorch, Transformers, PEFT |
+
+The model was initially fine-tuned using LoRA and Direct Preference Optimization (DPO). The LoRA adapter was subsequently merged into the base model using `merge_and_unload()` to produce a standalone model.
+
+## Dataset
+
+
+Training was performed using the **`train_pref` split of NVIDIA's When2Call dataset**.
+
+When2Call is designed specifically to evaluate and train LLMs on decisions about **when (and when not) to call tools**. The dataset includes preference pairs consisting of a chosen and rejected response for a given user request and tool specification.
+
+The `train_pref` split contains **9,000 preference-training examples** with:
+
+* Tool specifications
+* User messages
+* Chosen responses
+* Rejected responses
+
+NVIDIA provides both an SFT dataset (`train_sft`) and a preference dataset (`train_pref`); this model uses the **preference dataset for DPO training**.
+
+The When2Call dataset is synthetic and is licensed under **CC BY 4.0**.
+
+## Training Objective
+
+The objective was to improve the model's **tool-use decision boundary**.
+
+The model learns to distinguish between:
+
+1. **Tool Call** — a tool should be invoked to answer the request.
+2. **Request for Information** — the request can be handled without invoking a tool.
+3. **Cannot Answer** — the available tools cannot answer the request.
+
+The preference-training setup encourages the model to prefer appropriate responses over incorrect tool-calling behavior.
+
+## Evaluation
+
+The fine-tuned model was evaluated against the original Qwen2.5-1.5B-Instruct model on **300 samples**.
+
+The evaluation uses the same 300-sample LLM-as-a-judge subset provided by When2Call. NVIDIA's dataset contains a larger 3,652-sample MCQ test set and a 300-sample LLM-as-a-judge subset.
+
+### Results
+
+| Metric                   | Qwen2.5-1.5B-Instruct | Qwen2.5-1.5B + DPO |
+| ------------------------ | --------------------: | -----------------: |
+| Intent Accuracy          |                 52.7% |          **74.0%** |
+| Tool Precision           |                 40.8% |          **72.9%** |
+| Tool Recall              |             **93.0%** |              35.0% |
+| Tool F1                  |             **56.7%** |              47.3% |
+| Argument F1              |             **71.9%** |              67.7% |
+| Unsupported Tool Calls ↓ |                 45.0% |           **4.3%** |
+| Missed Tool Calls ↓      |              **2.3%** |              21.7% |
+| Throughput               |            38.3 tok/s |         26.4 tok/s |
+
+### Key Results
+
+The largest improvement was in **unsupported tool calls**:
+
+**45.0% → 4.3%**
+
+This indicates that DPO substantially reduced inappropriate or hallucinated tool invocations.
+
+Intent accuracy also increased:
+
+**52.7% → 74.0%**
+
+and tool precision increased:
+
+**40.8% → 72.9%**
+
+However, this came with a substantial reduction in tool recall:
+
+**93.0% → 35.0%**
+
+and Tool F1:
+
+**56.7% → 47.3%**
+
+Therefore, the main behavioral change is a shift toward a **more conservative, precision-oriented tool-calling policy**.
+
+## Confusion Matrix
+
+### Qwen2.5-1.5B-Instruct
+
+| Ground Truth \ Prediction | Tool | Request | Refusal |
+| ------------------------- | ---: | ------: | ------: |
+| Tool Call                 |   93 |       3 |       4 |
+| Request For Information   |   76 |      23 |       1 |
+| Cannot Answer             |   59 |      22 |      19 |
+
+### Qwen2.5-1.5B + DPO
+
+| Ground Truth \ Prediction | Tool | Request | Refusal |
+| ------------------------- | ---: | ------: | ------: |
+| Tool Call                 |   35 |      65 |       0 |
+| Request For Information   |   12 |      86 |       2 |
+| Cannot Answer             |    1 |      91 |       8 |
+
+The confusion matrix shows that DPO significantly reduced the model's tendency to issue tool calls for requests that should not result in a tool invocation.
+
+## Tool Calling Example
+
+### Tool Definition
+
+```python
+tools = [{
+    "name": "get_stock_price",
+    "description": "Fetch real-time stock price for a given ticker symbol.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "ticker": {
+                "type": "string",
+                "description": "The ticker symbol (e.g., AAPL, NVDA)"
+            }
+        },
+        "required": ["ticker"]
+    }
+}]
+```
+
+### User
+
+```text
+Can you check Nvidia's current stock price?
+```
+
+### Model Output
+
+```text
+<tool_call>
+{"name": "get_stock_price", "arguments": {"ticker": "NVDA"}}
+</tool_call>
+```
+
+## Inference
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "YOUR_USERNAME/YOUR_MODEL_NAME"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+model.eval()
+
+tools = [{
+    "name": "get_stock_price",
+    "description": "Fetch real-time stock price for a given ticker symbol.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "ticker": {
+                "type": "string",
+                "description": "The ticker symbol (e.g., AAPL, NVDA)"
+            }
+        },
+        "required": ["ticker"]
+    }
+}]
+
+messages = [
+    {
+        "role": "user",
+        "content": "Can you check Nvidia's current stock price?"
+    }
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tools=tools,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=128,
+        do_sample=False,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
+    )
+
+response = tokenizer.decode(
+    outputs[0][inputs.input_ids.shape[1]:],
+    skip_special_tokens=False
+)
+
+print(response)
+```
+
+## Output Post-Processing
+
+During evaluation, model outputs were passed through a lightweight post-processing step to normalize tool-call formatting.
+
+The post-processing:
+
+1. Extracts JSON containing `name` and `arguments`.
+2. Removes duplicate or nested `<tool_call>` wrappers.
+3. Normalizes the output to:
+
+```text
+<tool_call>
+{"name": "...", "arguments": {...}}
+</tool_call>
+```
+
+If no tool-call JSON is detected, the output is treated as a normal conversational response.
+
+The post-processing step is used for **format normalization and evaluation** and does not generate a tool call that the model did not produce.
+
+## Limitations
+
+The main limitation is the precision-recall trade-off introduced by DPO.
+
+The model is considerably better at avoiding unsupported tool calls, but it also misses a larger proportion of valid tool-call opportunities.
+
+Therefore, this model should not be interpreted as universally better than the base model for tool calling. Instead, it demonstrates that preference optimization can strongly shift the **tool-use decision policy** of a small instruction-tuned model.
+
+The evaluation also uses a relatively small 300-sample subset, so additional evaluation on larger and more diverse tool-calling benchmarks would be useful.
+
+## Intended Use
+
+This model is intended for research and experimentation involving:
+
+* Tool calling
+* Function calling
+* Tool-selection policies
+* Preference optimization
+* DPO
+* Agentic LLM systems
+* Small language model alignment
+
+It is **not intended to be considered production-ready** without additional task-specific evaluation.
+
+## Future Work
+
+* Recover tool-call recall while maintaining low unsupported-call rates
+* Experiment with DPO hyperparameters
+* Improve preference-data construction
+* Compare DPO against SFT
+* Evaluate larger Qwen models
+* Evaluate multi-tool selection
+* Evaluate multi-step tool calling
+* Improve argument-generation accuracy
+* Benchmark inference using vLLM
+* Evaluate on larger tool-calling benchmarks
+
+## Base Model
+
+This model is based on:
+
+**Qwen/Qwen2.5-1.5B-Instruct**
+
+Please refer to the base model for its original capabilities, license, and usage restrictions.
+
+## Dataset Citation
+
+This work uses NVIDIA's **When2Call** dataset.
+
+> Ross, Hayley, Ameya Sunil Mahabaleshwarka, and Yoshi Suhara. "When2Call: When (not) to Call Tools." NAACL 2025.
+
+```bibtex
+@inproceedings{ross-etal-2025-when2call,
+    title = "{W}hen2{C}all: When (not) to Call Tools",
+    author = "Ross, Hayley  and
+      Mahabaleshwarkar, Ameya Sunil  and
+      Suhara, Yoshi",
+    editor = "Chiruzzo, Luis  and
+      Ritter, Alan  and
+      Wang, Lu",
+    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
+    month = apr,
+    year = "2025",
+    address = "Albuquerque, New Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.naacl-long.174/",
+    doi = "10.18653/v1/2025.naacl-long.174",
+    pages = "3391--3409",
+    ISBN = "979-8-89176-189-6",
+    abstract = "Leveraging external tools is a key feature for modern Language Models (LMs) to expand their capabilities and integrate them into existing systems. However, existing benchmarks primarily focus on the accuracy of tool calling{---}whether the correct tool is called with the correct parameters{---}and less on evaluating when LMs should (not) call tools. We develop a new benchmark, When2Call, which evaluates tool-calling decision-making: when to generate a tool call, when to ask follow-up questions and when to admit the question can{'}t be answered with the tools provided. We find that state-of-the-art tool-calling LMs show significant room for improvement on When2Call, indicating the importance of this benchmark. We also develop a training set for When2Call and leverage the multiple-choice nature of the benchmark to develop a preference optimization training regime, which shows considerably more improvement than traditional fine-tuning. We release the benchmark and training data as well as evaluation scripts."
+}
+```
+
+## Acknowledgements
+
+Thanks to the NVIDIA When2Call authors and the Qwen team for releasing the dataset and base model used in this experiment.
--- a/chat_template.jinja
+++ b/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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,61 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.0.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:247e0b10761b18bbac6b60bbd80f2f05ac302bf19c8dab8c1e559d7004b3a814
+size 3087466808
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,29 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}