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Hammer2.1-3b

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Hammer2.1-3b is an open-weights language model from MadeAgents, released December 2024. Its published size is 3.09B parameters. Licence: other (qwen-research).

Catalogue identity notes

Maker repo MadeAgents/Hammer2.1-3b @702ce42: licence tag other (qwen-research). 3,085,383,680 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).

51.1±3.9#412 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 Hammer2.1-3b, out of 1132 with a score, then more from MadeAgents.

#modelscorewhereRAMreleased
538Command A Translate (08-2025) Cohere51±5api price unknown2025-08
539xLAM-2-1b-fc-r Salesforce51±4local~2.6GB2025-03
540Hammer2.1-3b MadeAgents51±4local~3.3GB2024-12
541DeepSeek-TNG R1T2 Chimera TNG Technology Consulting51±10local~229GB2025-07
542Llama 3.1 Nemotron 51B Instruct NVIDIA51±2local~34GB2024-09
more from MadeAgents
497Hammer2.1-7b MadeAgents53±4local~5.9GB2024-12
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▁▁▃▆█▆▅▄▄▃▂▁ #234 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, LFM2.5 1.2B Thinking here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)29.7% ✎▁▁▁▂██▅▅▆▂▂▂ beats 30/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 62%at its level
BFCL v359.6% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 81/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇ 31%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 Hammer2.1-3b · needs ~1.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Hammer2.1-3b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Hammer2.1-3b-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 Hammer2.1-3b · needs ~1.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Hammer2.1-3b-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 Hammer2.1-3b · needs ~1.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Hammer2.1-3b-GGUF:Q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Hammer2.1-3b · needs ~1.7 GB, Mac only · from huggingface.co/andosen
    chat in the terminal
    mlx_lm.chat --model andosen/Hammer2.1-3b-mlx-4Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model andosen/Hammer2.1-3b-mlx-4Bit

Other ways to run it

  1. Run it as published · needs ~6.17 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/Hammer2.1-3b', 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= 51.1

$ improve this page

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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