101 lines
4.7 KiB
Markdown
101 lines
4.7 KiB
Markdown
---
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base_model: meta-llama/Llama-3.2-1B-Instruct
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library_name: transformers
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tags:
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- function-calling
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- tool-use
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- llama
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- lora
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- bfcl
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license: llama3.2
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language:
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- en
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---
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# Llama-3.2-1B-Instruct — Function Calling (Refusal-SFT, "Path B")
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**Code & writeup:** https://github.com/keitake123/llama-function-calling-study
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The [v1 SFT model](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-full) further fine-tuned on a mix of call-required examples and **targeted refusal examples**, to recover irrelevance detection. **This is the effective fix** in a four-model study — and it succeeded where GRPO did not.
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## Results (BFCL v4)
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| Category | Base | v1 SFT | GRPO | This model (Path B) |
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|---|---|---|---|---|
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| irrelevance | 35.8 | 5.8 | 5.4 | **21.7** |
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| live_irrelevance | 67.3 | 16.9 | 17.2 | **36.8** |
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| simple_python | 75.0 | 77.5 | 77.2 | 78.2 |
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| multiple | 50.5 | 74.0 | 74.0 | 72.0 |
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| live_simple | 31.8 | 57.0 | 56.2 | 54.3 |
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| live_multiple | 7.3 | 38.8 | 39.4 | 35.7 |
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| live_relevance | 43.8 | 93.8 | 93.8 | 81.2 |
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**Finding:** Supervised refusal examples recovered irrelevance ~4x (5.8→21.7) and more than doubled live_irrelevance (16.9→36.8), at a modest, quantified cost to call-required categories (multiple −2, live_simple −2.7, live_multiple −3, live_relevance −12.5). This is a clean precision/recall tradeoff, and a much larger recovery than GRPO achieved (which showed no gain).
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**Takeaway:** for recovering an out-of-distribution behavior in a small model, targeted supervised demonstration outperforms RL reward shaping — because RL requires the target behavior to appear in sampled rollouts, which a strong SFT prior prevents.
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## Training details
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- **Base:** v1 SFT model · **Method:** LoRA SFT
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- **Data:** 1600 examples, 50/50 mix of xLAM call-required + synthetic refusal examples (query + unrelated tools → conversational refusal)
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- **Config:** 3 epochs, effective batch 16, lr 1.2e-4 cosine, bf16 · **final eval loss:** 0.17
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("Keitsuna123/llama-3.2-1b-fc-pathb")
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model = AutoModelForCausalLM.from_pretrained(
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"Keitsuna123/llama-3.2-1b-fc-pathb", torch_dtype=torch.bfloat16, device_map="auto"
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)
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# Call-required: produces a function call
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messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
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# Irrelevant: produces a conversational refusal instead of a hallucinated call
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# messages = [{"role": "user", "content": "How do I make bread fluffier?"}]
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tools = [{"type": "function", "function": {
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"name": "get_weather", "description": "Get the weather for a location",
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"parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}
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}}]
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text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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print(tokenizer.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
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```
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## Limitations
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- Residual irrelevance failures concentrate in **short, factual-seeming queries** ("what's 2+2", "capital of France") where a loosely-related tool exists — the hardest irrelevance sub-case.
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- Parallel calls remain near-zero (single-call training only).
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## Part of a series
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| Model | Description |
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|---|---|
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| [fc-sft-full](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-full) | v1 SFT on xLAM |
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| [fc-sft-v2-merged](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-v2-merged) | SFT on xLAM + distilabel (data-scaling ablation) |
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| [fc-grpo](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-grpo) | GRPO for irrelevance recovery (negative result) |
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| [fc-pathb](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-pathb) | Supervised refusal training (this model) |
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## References & Acknowledgements
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- **Base model:** Llama 3.2 (Meta AI) — [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
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- **Training data:** Salesforce xLAM — [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); refusal targets generated for this study
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- **Benchmark:** Berkeley Function Calling Leaderboard (BFCL) — [Gorilla project](https://github.com/ShishirPatil/gorilla)
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- **Frameworks:** HuggingFace TRL, PEFT, Transformers
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## Citation
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```bibtex
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@misc{taketsuna2026_fc_smallmodel,
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title = {Small-Model Function Calling: Comparing SFT, Data Scaling, GRPO, and Supervised Refusal Training},
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author = {Taketsuna, Keiichi},
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year = {2026},
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howpublished = {\url{https://huggingface.co/Keitsuna123}},
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note = {Llama-3.2-1B function-calling post-training study on BFCL v4}
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}
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``` |