whatmodel.sh / Llama-xLAM-2-70b-fc-r

Llama-xLAM-2-70b-fc-r

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Llama-xLAM-2-70b-fc-r is an open-weights language model from Salesforce, released March 2025. Its published size is 70.55B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo Salesforce/Llama-xLAM-2-70b-fc-r @783caf5: licence tag cc-by-nc-4.0. 70,553,706,496 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-03: month the maker's Hugging Face repo was created (createdAt 2025-03-25).

66.3±3.9#133 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 Llama-xLAM-2-70b-fc-r, out of 1132 with a score, then more from Salesforce.

#modelscorewhereRAMreleased
168GPT-5.1-Codex OpenAI66±2comparison only2025-11
169Grok 3 Preview (02-24) SpaceXAI (xAI)66±3comparison only2025-02
170Llama-xLAM-2-70b-fc-r Salesforce66±4local~29GB2025-03
171Qwen3.6 Flash Alibaba66±1api $0.56/M2026-04
172MiMo-V2-Flash (309B-A15B) Xiaomi66±1local~108GB2025-12
more from Salesforce
165xLAM-2-32b-fc-r Salesforce66±4local~21GB2025-03
256Llama-xLAM-2-8b-fc-r Salesforce62±4local~6.6GB2025-03
350xLAM-2-3b-fc-r Salesforce58±4local~3.5GB2025-03

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #136 of 334 * · 93% of its score · 2 tests

Beats Gemini 2.5 Pro, watt-tool-70B, DeepSeek V3.2 Exp here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Large, GPT-4o (2024-05-13) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)53.1% ✎▁▁▁▂██▅▅▆▂▂▂ beats 60/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 67%at its level
BFCL v378.5% ✎▁▁▁▁▁▃▅█▅▄▁▃ beats 114/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇ 26%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 Llama-xLAM-2-70b-fc-r · needs ~42.5 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Llama-xLAM-2-70b-fc-r-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Llama-xLAM-2-70b-fc-r-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 Llama-xLAM-2-70b-fc-r · needs ~42.5 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Llama-xLAM-2-70b-fc-r-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 Llama-xLAM-2-70b-fc-r · needs ~42.5 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Llama-xLAM-2-70b-fc-r-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~141.11 GB · from huggingface.co/Salesforce (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Salesforce/Llama-xLAM-2-70b-fc-r', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Salesforce ↗

$ 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= 66.3

$ 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