whatmodel.sh / ToolACE-8B
ToolACE-8B
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ToolACE-8B is an open-weights language model from Team-ACE (Huawei Noah's Ark & USTC), released August 2024. Its published size is 8.03B parameters. Licence: apache-2.0.
Catalogue identity notes
Maker repo Team-ACE/ToolACE-8B @e9ee4e0: licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-08: month the maker's Hugging Face repo was created (createdAt 2024-08-29).
$ how it compares
The LLMs ranked either side of ToolACE-8B, out of 1132 with a score, then more from Team-ACE (Huawei Noah's Ark & USTC).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 428 | Llama-3-8B-Instruct-RainbowPO | 55±2 | local | ||
| 429 |  Olmo 3.1 32B Instruct | 55±2 | local | ||
| 430 | ToolACE-8B | 55±6 | local | ||
| 431 |  AntAngelMed | 55±4 | local | ||
| 432 |  HuatuoGPT-o1-70B | 55±4 | local | ||
| more from Team-ACE (Huawei Noah's Ark & USTC) | |||||
| 307 | ToolACE-2-Llama-3.1-8B | 59±4 | local | ||
$ what it's good at
agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #238 of 334 * · 81% of its score · 1 test
Beats o1 (2024-12-17), o3-mini, Llama 3.1 Nemotron Ultra 253B v1 here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Mini, LFM2.5 1.2B Thinking here, though they rank lower overall.
$ where to run it
llama.cpp · recommended
- 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 | shWindowsirm https://llama.app/install.ps1 | iex - Run ToolACE-8B · needs ~4.9 GB · from huggingface.co/Nekuromentochat in the terminal
llama cli -hf Nekuromento/ToolACE-8B-Q4_K_M-GGUF:q4_k_m - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf Nekuromento/ToolACE-8B-Q4_K_M-GGUF:q4_k_m
LM Studio
- Install LM Studio · Open the app once before using the lms command.
- Run ToolACE-8B · needs ~4.9 GB · from huggingface.co/Nekuromento
- Or download it in the terminal
lms get https://huggingface.co/Nekuromento/ToolACE-8B-Q4_K_M-GGUF@q4_k_m -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run ToolACE-8B · needs ~4.9 GB · from huggingface.co/Nekuromentochat in the terminal
ollama run hf.co/Nekuromento/ToolACE-8B-Q4_K_M-GGUF:q4_k_m
Other ways to run it
- 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-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… 81%
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
| source | results | share of the score |
|---|---|---|
 Berkeley Function Calling Leaderboard result archive | 1 | 81% |
| prior · pull toward the middle | 19% | |
| weighted average | = 54.7 |
$ improve this page
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data updated · sources and licences · json