whatmodel.sh / ToolACE-2-Llama-3.1-8B

ToolACE-2-Llama-3.1-8B

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ToolACE-2-Llama-3.1-8B is an open-weights language model from Team-ACE (Huawei Noah's Ark & USTC), released March 2025. Its published size is 8.03B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo Team-ACE/ToolACE-2-Llama-3.1-8B @06c43a3 (requested as Team-ACE/ToolACE-2-8B, which the HF API redirects to Team-ACE/ToolACE-2-Llama-3.1-8B): licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-03: month the maker's Hugging Face repo was created (createdAt 2025-03-12).

59.1±3.8#233 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of ToolACE-2-Llama-3.1-8B, out of 1132 with a score, then more from Team-ACE (Huawei Noah's Ark & USTC).

#modelscorewhereRAMreleased
305Hunyuan-TurboS (20250226) Tencent59±3comparison only2025-02
306Amazon Nova Experimental Chat (10-20) Amazon59±3comparison only
307ToolACE-2-Llama-3.1-8B Team-ACE (Huawei Noah's Ark & USTC)59±4local~6.6GB2025-03
308CoALM-405B UIUC ConvAI & Oumi (CoALM)59±7local~854GB2025-02
309Gemma 2 9B IT DPO (Princeton) Princeton NLP59±1local~7.6GB2024-07
more from Team-ACE (Huawei Noah's Ark & USTC)
430ToolACE-8B Team-ACE (Huawei Noah's Ark & USTC)55±6local~6.6GB2024-08

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #171 of 334 * · 93% of its score · 2 tests

Beats o1 (2024-12-17), o3-mini, DeepSeek R1 0528 here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Mini, Jamba 1.5 Large here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)42.4% ✎▁▁▁▂██▅▅▆▂▂▂ beats 48/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 65%at its level
BFCL v368.7% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 106/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇ 28%blunt · at its level

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run ToolACE-2-Llama-3.1-8B · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/ToolACE-2-Llama-3.1-8B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/ToolACE-2-Llama-3.1-8B-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run ToolACE-2-Llama-3.1-8B · needs ~4.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/ToolACE-2-Llama-3.1-8B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run ToolACE-2-Llama-3.1-8B · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/ToolACE-2-Llama-3.1-8B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/Team-ACE (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Team-ACE/ToolACE-2-Llama-3.1-8B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Team-ACE ↗

$ how the score was worked out · Berkeley Function Calling Lea… 93%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Berkeley Function Calling Leaderboard result archive2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%Apache-2.0
prior · pull toward the middle▇▇ 7%
weighted average= 59.1

$ improve this page

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