whatmodel.sh / Llama-xLAM-2-8b-fc-r

Llama-xLAM-2-8b-fc-r

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Llama-xLAM-2-8b-fc-r is an open-weights language model from Salesforce, released March 2025. Its published size is 8.03B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo Salesforce/Llama-xLAM-2-8b-fc-r @a0efe39: licence tag cc-by-nc-4.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-03: month the maker's Hugging Face repo was created (createdAt 2025-03-27).

61.8±3.9#198 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-2-8b-fc-r, out of 1132 with a score, then more from Salesforce.

#modelscorewhereRAMreleased
254Gemini 2.0 Flash-Lite (001) Google62±2comparison only2025-02
255Cogito v1 Preview Qwen 32B Deep Cogito62±7local~21GB2025-03
256Llama-xLAM-2-8b-fc-r Salesforce62±4local~6.6GB2025-03
257Mistral Large 3 (675B-A41B) Mistral62±1api $0.75/M +local~108GB2025-12
258Apriel 1.6 15B Thinker ServiceNow62±2local~10GB2025-11
more from Salesforce
165xLAM-2-32b-fc-r Salesforce66±4local~21GB2025-03
170Llama-xLAM-2-70b-fc-r Salesforce66±4local~29GB2025-03
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▁▁▃▆█▆▅▄▄▃▂▁ #159 of 334 * · 93% of its score · 2 tests

Beats o1 (2024-12-17), o3-mini, DeepSeek R1 0528 here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Mini, Jamba 1.5 Large here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL V4 (December 2025 archive)46.7% ✎▁▁▁▂██▅▅▆▂▂▂ beats 53/77hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 66%at its level
BFCL v372.0% ✎▁▁▁▁▁▃▅█▅▄▃▁ beats 111/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇ 27%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-2-8b-fc-r · needs ~4.9 GB · from huggingface.co/Salesforce (official)
    chat in the terminal
    llama cli -hf Salesforce/Llama-xLAM-2-8b-fc-r-gguf:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Salesforce/Llama-xLAM-2-8b-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-2-8b-fc-r · needs ~4.9 GB · from huggingface.co/Salesforce (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Salesforce/Llama-xLAM-2-8b-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-2-8b-fc-r · needs ~4.9 GB · from huggingface.co/Salesforce (official)
    chat in the terminal
    ollama run hf.co/Salesforce/Llama-xLAM-2-8b-fc-r-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 Llama-xLAM-2-8b-fc-r · needs ~8.5 GB, Mac only · from huggingface.co/mxmcc
    chat in the terminal
    mlx_lm.chat --model mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit

Other ways to run it

  1. Run it as published · needs ~16.06 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-2-8b-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= 61.8

$ 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