whatmodel.sh / Llama-3-8B-Instruct-Gradient-1048k

Llama-3-8B-Instruct-Gradient-1048k

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Llama-3-8B-Instruct-Gradient-1048k is an open-weights language model from Gradient AI, released April 2024. Its published size is 8.03B parameters. Licence: llama3.

Catalogue identity notes

Maker repo gradientai/Llama-3-8B-Instruct-Gradient-1048k @cd3069b: licence tag llama3. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-29).

46.7±5.4#532 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-3-8B-Instruct-Gradient-1048k, out of 1132 with a score, then more from Gradient AI.

#modelscorewhereRAMreleased
672Magicoder-S-CL-7B UIUC (ise-uiuc)47±1local~5.8GB2023-12
673Llama-3-8B-Magpie-Align-v0.1 Magpie-Align47±3local~6.6GB2024-06
674Llama-3-8B-Instruct-Gradient-1048k Gradient AI47±5local~6.2GB2024-04
675Aya Expanse 8B Cohere47±2local~6.2GB2024-10
676Phind CodeLlama 34B v2 Phind47±1local~23GB2023-08
more from Gradient AI
592Llama-3-70B-Instruct-Gradient-1048k Gradient AI50±6local~29GB2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 87% of its score
testresultvs the others in its rundifficultycountswhy
RULER 64k76.3% ✎▁▁·▁·▃▃▄█▂·▁ beats 4/22easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%typical
RULER 128k69.5% ✎▄··▂▄▂▆██▄▄▂ beats 11/21easy · avg 68%▇▇▇▇▇▇▇▇▇ 23%at its level · noisy

$ 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-3-8B-Instruct-Gradient-1048k · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Llama-3-8B-Instruct-Gradient-1048k-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/Llama-3-8B-Instruct-Gradient-1048k-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-3-8B-Instruct-Gradient-1048k · needs ~4.9 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-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-3-8B-Instruct-Gradient-1048k · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-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 Llama-3-8B-Instruct-Gradient-1048k · needs ~4.5 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Llama-3-8B-Instruct-Gradient-1048k-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Llama-3-8B-Instruct-Gradient-1048k-4bit

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/gradientai (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='gradientai/Llama-3-8B-Instruct-Gradient-1048k', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/gradientai ↗

$ how the score was worked out · NVIDIA RULER repository READM… 87%

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
NVIDIA RULER repository README results table2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 87%Apache-2.0
prior · pull toward the middle▇▇▇▇ 13%
weighted average= 46.7

$ 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