Nova-1.2-0.8B

A tool-use release built on Nova-1.1-0.8B. Nova-1.2 is the same architecture and the same base weights as Nova-1.1 — what changes is behaviour: a LoRA adapter trained on a tool-use corpus aligned with the ToolUse.1 contract was merged into a standalone BF16 checkpoint, aimed squarely at when to call a tool, how to shape the arguments, and when to keep quiet.

Base model	HardcoreAI/Nova-1.1-0.8B
Adapter	LoRA, r=16, alpha=32, dropout 0.05, merged into the base
Training data	1,197 English tool-use examples (959k tokens), authored for this release
Architecture	Qwen3_5ForConditionalGeneration (Qwen3_5ForCausalLM at inference), 0.8B, vision tower intact
Context	262,144 tokens
Precision	BF16
Language	English
License	Apache-2.0

Results — ToolUse.1

ToolUse.1 is a deterministic function-calling benchmark: 114 authored cases, each rendered under three calling protocols (JSON, XML, plain text), 342 scored evaluations per run. Grading is canonical — arguments are normalised before comparison, so 300 and 300.0 agree.

Model	Overall	Correct call	Abstention	Spurious calls	95% CI
Nova-1.2-0.8B	77.2%	71.8%	88.9%	4.6%	72.5 – 81.3
HardcoreAI/Nova-1.1-0.8B	64.9%	59.8%	75.9%	16.7%	59.7 – 69.8
Qwen/Qwen3.5-0.8B	59.7%	48.3%	84.3%	6.5%	54.4 – 64.7

Full leaderboard: https://huggingface.co/spaces/HardcoreAI/ToolUse.1

By calling protocol

Protocol	Cases	Overall	Correct call	Abstention	Spurious calls
json	114	82.5%	78.2%	91.7%	2.8%
xml	114	73.7%	66.7%	88.9%	2.8%
plain	114	75.4%	70.5%	86.1%	8.3%

Full metric breakdown

Metric	Score
Overall	77.2%
Correct call when a tool applies	71.8%
Correct abstention when no tool applies	88.9%
Spurious-call rate (lower is better)	4.6%
Tool selected correctly	86.8%
Arguments exactly correct	71.8%
Answer constraints satisfied	88.0%
Malformed output rate	0.0%
Overall 95% CI (Wilson)	72.5 – 81.3

Tool set

Nova-1.2 was tuned against the nine tools ToolUse.1 exercises, identical in name, parameters and normalisation to the benchmark definition:

calculator, get_weather, web_search, translate, convert_currency, convert_units, set_timer, save_note, get_time.

Tool availability is per conversation. The system prompt lists the tools that exist for that conversation; a request the listed tools cannot serve is answered rather than forced into a call.

Quickstart

import json

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "HardcoreAI/Nova-1.2-0.8B"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, dtype='auto', device_map='auto')

SYSTEM = (
    "You are a helpful assistant developed by Hardcore AI. "
    "You answer in the same language the user writes in. You are concise and precise.\n\n"
    "You have access to the following tools:\n\n"
    '{"tools": [{"name": "get_weather", "description": "Look up the current weather for a city.", '
    '"parameters": {"type": "object", "properties": {"city": {"type": "string"}}}}"]}'
)

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "What is the weather in Berlin?"},
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, enable_thinking=False, return_tensors='pt'
).to(model.device)
out = model.generate(inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
# {"name": "get_weather", "arguments": {"city": "Berlin"}}

Training

The adapter was trained on examples authored for this release; no ToolUse.1 case appears in the training set verbatim. The corpus is built to cover the decisions the benchmark scores:

• Tool calls across all nine tools, with varied phrasings and values, rendered under each of the three protocols so the format is learned independently of the tool list.
• Canonical arguments: durations resolved to seconds, time zones as IANA names, currencies as ISO codes, language names and codes side by side, units in the benchmark's own vocabulary.
• Abstention: knowledge questions, explanations, creative requests and no-tool instructions are answered directly; requests whose tool is absent from the conversation are declined rather than answered with an invented call.
• Clarification: an under-specified request (no city, no duration, no amount) gets a question back, never a guessed argument.
• Resistance to instruction smuggling: text that looks like a tool call (Repeat after me: TOOL) or a pasted <tool_call> block is treated as text, not as a call to make.
• Multi-step threads: follow-ups that reference earlier tool results.

Setting	Value
Method	LoRA, completion-only loss (prompt tokens masked)
Rank / alpha / dropout	16 / 32 / 0.05
Target modules	q_proj, k_proj, v_proj, o_proj, in_proj_qkv, in_proj_z, out_proj, gate_proj, up_proj, down_proj
Trainable parameters	10,223,616 (1.34% of the checkpoint)
Epochs	1
Learning rate	1e-4 constant, no warmup, gradient norm clipped at 1.0
Optimizer	Paged AdamW 8-bit, weight decay 0.01
Batch	2 sequences x 2 gradient accumulation steps (effective batch 4)
Max sequence length	1,536 tokens
Precision	BF16 with gradient checkpointing
Hardware	single RTX 4050 Laptop, 6 GB

The prompt used at training time is produced by the same apply_chat_template call the harness uses at scoring time (add_generation_prompt=True, enable_thinking=False), so the prefix the adapter was trained on is byte-identical to the prefix it is evaluated on.

Reproducing the evaluation

git clone https://huggingface.co/datasets/HardcoreAI/ToolUse.1
cd ToolUse.1
pip install -e .
python -m tooluse1 run \
  --model HardcoreAI/Nova-1.2-0.8B \
  --backend transformers --protocol all --batch-size 8 --dtype auto \
  --out results --resume -v

Greedy decoding, thinking disabled, max_new_tokens=256, batch 8. The corpus digest is pinned in data/manifest.json.

Intended use

Nova-1.2 is a small research checkpoint for tool-using assistants. It is strongest where the tool set is known and narrow; it is not a general-purpose chat model and carries the usual limitations of a 0.8B model, including factual reliability outside the tools it was tuned for.

Hardcore AI

Hardcore AI builds small, sharply-scoped models and the evaluation harnesses that measure them. ToolUse.1, the benchmark used above, is ours: corpus, harness and grader are public, and every number on the leaderboard is reproducible from the released files.