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Hammer2.1-7b

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Hammer2.1-7b is an open-weights language model from MadeAgents, released December 2024. Its published size is 7.61B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo MadeAgents/Hammer2.1-7b @c5692ee: licence tag cc-by-nc-4.0. 7,612,756,480 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-12: month the maker's Hugging Face repo was created (createdAt 2024-12-12).

52.7±3.9#383 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 Hammer2.1-7b, out of 1132 with a score, then more from MadeAgents.

#modelscorewhereRAMreleased
495Ministral 3 8B Mistral53±1api $0.15/M +local~6.5GB2025-12
496Qwen3-VL 4B Instruct Alibaba53±1local~4.1GB2025-10
497Hammer2.1-7b MadeAgents53±4local~5.9GB2024-12
498Gemini Experimental 1114 Google53±2comparison only2024-11
499Apertus 1.5 70B Swiss AI53±7local~153GB2026-07
more from MadeAgents
540Hammer2.1-3b MadeAgents51±4local~3.3GB2024-12
601Hammer2.1-1.5b MadeAgents49±4local~2.4GB2024-12
806Hammer2.1-0.5b MadeAgents43±4local~1.9GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #220 of 334 * · 93% of its score · 2 tests

Beats o1 (2024-12-17), o3-mini, DeepSeek R1 (Jan '25) here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Mini, Mellum2 12B-A2.5B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)31.7% ✎▁▁▁▂██▅▅▆▂▂▂ beats 33/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%at its level
BFCL v362.3% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 85/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇ 30%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 Hammer2.1-7b · needs ~4.2 GB · from huggingface.co/eaddario
    chat in the terminal
    llama cli -hf eaddario/Hammer2.1-7b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf eaddario/Hammer2.1-7b-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 Hammer2.1-7b · needs ~4.2 GB · from huggingface.co/eaddario

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/eaddario/Hammer2.1-7b-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 Hammer2.1-7b · needs ~4.2 GB · from huggingface.co/eaddario
    chat in the terminal
    ollama run hf.co/eaddario/Hammer2.1-7b-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~15.23 GB · from huggingface.co/MadeAgents (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='MadeAgents/Hammer2.1-7b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/MadeAgents ↗

$ 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= 52.7

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json