---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
tags:
- function-calling
- tool-use
- llama
- lora
- bfcl
license: llama3.2
language:
- en
---

# Llama-3.2-1B-Instruct — Function Calling (SFT on xLAM + distilabel)

**Code & writeup:** https://github.com/keitake123/llama-function-calling-study

Supervised fine-tune of `meta-llama/Llama-3.2-1B-Instruct` on a larger merged dataset (xLAM + Argilla distilabel). This is the **data-scaling ablation** in a four-model function-calling study — testing whether adding ~19k additional synthetic examples to xLAM improves results.

## Results (BFCL v4)

| Category | Base | v1 (xLAM only) | This model (v2, +distilabel) |
|---|---|---|---|
| simple_python | 75.0 | 77.5 | 79.5 |
| multiple | 50.5 | 74.0 | 77.5 |
| live_simple | 31.8 | 57.0 | 58.5 |
| live_multiple | 7.3 | 38.8 | 43.5 |
| live_relevance | 43.8 | 93.8 | 87.5 |
| live_irrelevance | 67.3 | 16.9 | 22.2 |
| irrelevance | 35.8 | 5.8 | 6.2 |
| parallel | 44.0 | 1.0 | 0.0 |
| parallel_multiple | 15.0 | 2.0 | 0.0 |

**Finding:** Adding more (mixed-quality) data gave marginal call-required gains (+1.5 to +4.7 pts) but **did not fix the irrelevance regression** (still ~6%). Volume is not the fix for irrelevance — see the [Path B model](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-pathb) for the effective approach. Note: ~12% of the distilabel additions had malformed JSON and were filtered out during preprocessing.

## Training details

- **Base:** meta-llama/Llama-3.2-1B-Instruct
- **Data:** xLAM + argilla/apigen (distilabel), filtered to 45k single-call examples
- **Method:** LoRA (r=16, α=32), 1 epoch, effective batch 16, lr 2e-4 cosine, bf16
- **Hardware:** 1× A100, ~98 min · **final eval loss:** 0.16

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("Keitsuna123/llama-3.2-1b-fc-sft-v2-merged")
model = AutoModelForCausalLM.from_pretrained(
    "Keitsuna123/llama-3.2-1b-fc-sft-v2-merged", torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
tools = [{"type": "function", "function": {
    "name": "get_weather", "description": "Get the weather for a location",
    "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}
}}]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
```

## Limitations

- Irrelevance detection still regressed vs. base. Parallel calls near-zero (single-call training only).

## Part of a series

| Model | Description |
|---|---|
| [fc-sft-full](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-full) | v1 SFT on xLAM |
| [fc-sft-v2-merged](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-sft-v2-merged) | SFT on xLAM + distilabel (this model) |
| [fc-grpo](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-grpo) | GRPO for irrelevance recovery (negative result) |
| [fc-pathb](https://huggingface.co/Keitsuna123/llama-3.2-1b-fc-pathb) | Supervised refusal training (effective irrelevance fix) |

## References & Acknowledgements

- **Base model:** Llama 3.2 (Meta AI) — [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
- **Training data:** Salesforce xLAM — [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); Argilla APIGen — [argilla/apigen-function-calling](https://huggingface.co/datasets/argilla/apigen-function-calling)
- **Benchmark:** Berkeley Function Calling Leaderboard (BFCL) — [Gorilla project](https://github.com/ShishirPatil/gorilla)
- **Frameworks:** HuggingFace TRL, PEFT, Transformers

## Citation

```bibtex
@misc{taketsuna2026_fc_smallmodel,
  title  = {Small-Model Function Calling: Comparing SFT, Data Scaling, GRPO, and Supervised Refusal Training},
  author = {Taketsuna, Keiichi},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/Keitsuna123}},
  note   = {Llama-3.2-1B function-calling post-training study on BFCL v4}
}
```