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Hammer2.1-1.5b

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Hammer2.1-1.5b is an open-weights language model from MadeAgents, released December 2024. Its published size is 1.54B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo MadeAgents/Hammer2.1-1.5b @62c20f5: licence tag cc-by-nc-4.0. 1,543,298,048 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).

49.0±4.0#468 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 Hammer2.1-1.5b, out of 1132 with a score, then more from MadeAgents.

#modelscorewhereRAMreleased
599Granite 4.1 3B IBM49±1local~3.7GB2026-04
600OpenCodeInterpreter-DS-6.7B M-A-P49±1local~5.8GB2024-02
601Hammer2.1-1.5b MadeAgents49±4local~2.4GB2024-12
602Artigenz-Coder-DS-6.7B Artigenz49±1local~5.8GB2024-04
603AFM-4.5B Arcee AI49±6local~4.6GB2025-07
more from MadeAgents
497Hammer2.1-7b MadeAgents53±4local~5.9GB2024-12
540Hammer2.1-3b MadeAgents51±4local~3.3GB2024-12
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▁▁▃▆█▆▅▄▄▃▂▁ #260 of 334 * · 93% of its score · 2 tests

Beats o3-mini, Ministral 3 14B Reasoning 2512, DeepSeek R1 0528 Qwen3 8B here, though they rank higher overall. Trails Granite 4.0 1B, SmolLM3 3B, Phi-3.5-mini-instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)27.9% ✎▁▁▁▂██▅▅▆▂▂▂ beats 24/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 61%at its level
BFCL v354.9% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 58/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%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 Hammer2.1-1.5b · needs ~1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Hammer2.1-1.5b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Hammer2.1-1.5b-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 Hammer2.1-1.5b · needs ~1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Hammer2.1-1.5b-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 Hammer2.1-1.5b · needs ~1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Hammer2.1-1.5b-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Hammer2.1-1.5b · needs ~0.9 GB, Mac only · from huggingface.co/smdesai
    chat in the terminal
    mlx_lm.chat --model smdesai/Hammer2.1-1.5b-4-bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model smdesai/Hammer2.1-1.5b-4-bit

Other ways to run it

  1. Run it as published · needs ~3.09 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/Hammer2.1-1.5b', 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= 49.0

$ 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