Built with PLaMo. This is, to our knowledge, the first open-weights PLaMo model with function calling. PFN ships tool calling only in its API-only PLaMo Prime line; all open PLaMo releases are base models. PLaMo 3's vocabulary, however, contains chat/structured-output control tokens and the base repos ship an official chat_template.jinja — this LoRA is trained directly on those rails.

Current revision = v2 (corrected data, 1,718 examples, 39 min on MPS): 100% parse / function / args-exact on the frozen 40 unseen call questions, 16.7% false-call on strict no-call cases. The original 400-example sprint adapter remains in checkpoint-400/. The v1 full-data run was halted deliberately after checkpoint evals exposed a training-label flaw — see "The v1 data flaw" below; the negative result is published, not hidden.

Results

Single-turn tool-calling eval: 40 call + 12 no-call unseen queries (zero verbatim overlap with training data — all rows below were measured on the rebuilt, leak-free eval set; the earlier contaminated eval was discarded and is not shown anywhere), greedy decoding, exact-match scoring:

Condition	Parse rate	Func-name acc	Args exact	False-call rate
base, zero-shot	35.0%	32.5%	0.0%	0.0%¹
base, 2-shot	67.5%	30.0%	0.0%	41.7%
+ this LoRA, 400 ex. (current revision)	92.5%	92.5%	90.0%	41.7%
+ LoRA v1 ckpt @ 3.2k ex.	87.5%	87.5%	87.5%	25.0%
+ LoRA v1 ckpt @ 6.4k ex. (run halted)	55.0%	55.0%	55.0%	16.7%
+ LoRA v2 (fixed data, 1,718 ex., 39 min)	100%	100%	100%	16.7%²

² v2 false-call: 12 strict no-call cases (call-free conversations); not comparable to v1 rows' column (label flaw, below). Its 2 false calls are tool-adjacent requests where no matching tool was listed (hallucinated function instead of declining). Call-side questions identical across all rows.

The v1 data flaw (why more data made it worse)

Checkpoint evals showed call recall degrading (90% → 87.5% → 55%) while precision of emitted calls stayed ~100% — the model could call perfectly but increasingly chose prose instead. Root cause: glaive conversations are multi-turn, and the assistant often first asks a clarifying question ("How long should the password be?") before calling the function in a later turn. Single-turn extraction labeled all such first replies "no-call" — 96% of the v1 no-call class — teaching the model that tool-worthy requests deserve polite prose. Under a strict definition (no call anywhere in the conversation), the entire 113k-row dataset contains only ~330 genuine no-call conversations. v2 trains on exactly those (318) plus 1,400 call examples; deferred-clarification turns are excluded.

¹ trivially low — the zero-shot base model rarely emits a call at all.

Per-question predictions for every condition are in eval/. An earlier eval measured 97.5% but was contaminated (41/52 queries leaked into training via glaive's row duplication); it was discarded and rebuilt — details in the project repo.

Usage

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "pfnet/plamo-3-nict-2b-base"   # gated: accept the PLaMo Community License
tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, dtype=torch.bfloat16)
model = PeftModel.from_pretrained(model, "Gurunath/plamo-3-nict-2b-tool-calling-lora")

messages = [
    {"role": "system", "content": (
        "You are a helpful assistant with access to the following tools. "
        "When the user's request requires a tool, respond with ONLY a JSON object "
        '{"name": <function-name>, "arguments": <args-object>}. '
        "If no tool is needed, answer normally.\nTools:\n"
        '{"name": "get_weather", "description": "Get current weather", '
        '"parameters": {"type": "object", "properties": {"location": {"type": "string"}}, '
        '"required": ["location"]}}')},
    {"role": "user", "content": "What's the weather in Osaka right now?"},
]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=64, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
# {"name": "get_weather", "arguments": {"location": "Osaka"}}

Training

Base: pfnet/plamo-3-nict-2b-base (2.6B, attention-only), official chat template
Data: 400 single-turn examples (350 call / 50 no-call) from glaiveai/glaive-function-calling-v2 (Apache 2.0), query-level deduplicated
LoRA r=16, α=32, all-linear, assistant-only loss masking, 1 epoch, bf16
Hardware: MacBook Pro M4 Pro (Apple MPS) — 5 minutes of training
Full pipeline (data prep, training, eval): github.com/rajagurunath/pfn-plamo-inference-study

Repository contents (for reproducibility)

Path	What
`/` (root)	v2 adapter — the release (fixed data, 100% args-exact on frozen eval)
`checkpoint-400/`	The 5-minute sprint adapter (400 examples, 90% args-exact)
`checkpoint-v1-step400/`, `checkpoint-v1-step800/`	Mid-training checkpoints of the halted v1 run (3,200 / 6,400 examples) — the models behind the degradation curve; load them to reproduce the data-flaw analysis
`eval/*.json`	Per-question results (question, gold, prediction) for all six evaluated conditions
`eval/data/eval-v2.jsonl`	The frozen 52-question eval set (40 call + 12 strict no-call) all table rows use
`eval/data/eval-v1.jsonl`	The v1-era eval set (deferred-style no-call cases), kept for comparison
`figures/`	The v1 degradation curve

All adapters share the same base (pfnet/plamo-3-nict-2b-base) and load with PeftModel.from_pretrained(model, REPO, subfolder=...).

License

Derivative of PLaMo 3 NICT 2B under the PLaMo Community License (see license_link). Non-commercial use is free; commercial use is subject to PFN's license terms — see the license and PFN's registration requirements. Training data is Apache 2.0.