whatmodel.sh / FsfairX-Zephyr-Chat-v0.1

FsfairX-Zephyr-Chat-v0.1

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FsfairX-Zephyr-Chat-v0.1 is an open-weights language model from sfairXC (RLHFlow), released April 2024. Its published size is 7.24B parameters. Licence: cc-by-sa-4.0.

Catalogue identity notes

Maker repo sfairXC/FsfairX-Zephyr-Chat-v0.1 @e585ddb: licence tag cc-by-sa-4.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-20).

48.5±1.7#486 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of FsfairX-Zephyr-Chat-v0.1, out of 1132 with a score.

#modelscorewhereRAMreleased
620Magicoder-S-DS 6.7B UIUC (ise-uiuc)49±1local~5.8GB2023-12
621InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)49±2local~6.1GB2024-06
622FsfairX-Zephyr-Chat-v0.1 sfairXC (RLHFlow)49±2local~6.1GB2024-04
623Command R7B Arabic (02-2025) Cohere49±5local~6.9GB2025-02
624Hermes 3 Llama 3.1 70B Nous Research49±1local~44GB2024-07

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #294 of 466 * · 99% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Ministral 3 14B, Gemma 4 E4B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 1.2B, Granite 4.0 H 1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled34.8% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 88/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%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 FsfairX-Zephyr-Chat-v0.1 · needs ~4.4 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    llama cli -hf featherless-ai-quants/sfairXC-FsfairX-Zephyr-Chat-v0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf featherless-ai-quants/sfairXC-FsfairX-Zephyr-Chat-v0.1-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 FsfairX-Zephyr-Chat-v0.1 · needs ~4.4 GB · from huggingface.co/featherless-ai-quants

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/featherless-ai-quants/sfairXC-FsfairX-Zephyr-Chat-v0.1-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 FsfairX-Zephyr-Chat-v0.1 · needs ~4.4 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    ollama run hf.co/featherless-ai-quants/sfairXC-FsfairX-Zephyr-Chat-v0.1-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~14.48 GB · from huggingface.co/sfairXC (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='sfairXC/FsfairX-Zephyr-Chat-v0.1', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/sfairXC ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 48.5

$ 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