whatmodel.sh / Meditron 70B
Meditron 70B
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Meditron 70B is an open-weights language model from EPFL LLM, released November 2023. Its published size is 68.98B parameters. Licence: llama2.
Catalogue identity notes
Maker repo epfl-llm/meditron-70b @3b41343: licence tag llama2. 68,976,926,720 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-11: month the maker's Hugging Face repo was created (createdAt 2023-11-08). Pretrained base checkpoint (not an instruct/chat version). Gated repository (licence acceptance required on Hugging Face).
$ how it compares
The LLMs ranked either side of Meditron 70B, out of 1132 with a score, then more from EPFL LLM.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 906 |  Llama 3.2 3B Instruct | 38±1 | api $0.12/M +local | ||
| 907 |  Qwen-14B-Chat | 38±1 | local | ||
| 908 | Meditron 70B | 38±4 | local | ||
| 909 |  Jais Family 2p7b (2.7B) Chat | 38±4 | local | ||
| 910 |  Mistral-ORPO-beta | 38±2 | local | ||
| more from EPFL LLM | |||||
| 1102 | Meditron 7B | 18±6 | local | ||
$ what it's good at
other tests2 not in a task · 92% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BRIDGE clinical text, zero-shot average (87 tasks) | 15.7% ✎ | ▁▁▁▃▁▂▂▃▄▆█▃ beats 4/97 | 64% |
| SurgiQ surgical MCQ (zero-shot, log-probability scoring) | 56.5% ✎ | ▂···▂▂·▃▂▃██ beats 8/17 | 28% |
$ where to run it
llama.cpp · recommended
version
- 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 | shWindowsirm https://llama.app/install.ps1 | iex - Run Meditron 70B · needs ~41.4 GB · from huggingface.co/TheBlokechat in the terminal
llama cli -hf TheBloke/meditron-70B-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf TheBloke/meditron-70B-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Meditron 70B · needs ~41.4 GB · from huggingface.co/TheBloke
- Or download it in the terminal
lms get https://huggingface.co/TheBloke/meditron-70B-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
version
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run Meditron 70B · needs ~41.4 GB · from huggingface.co/TheBlokechat in the terminal
ollama run hf.co/TheBloke/meditron-70B-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~137.95 GB · from huggingface.co/epfl-llm (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='epfl-llm/meditron-70b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/epfl-llm ↗
$ how the score was worked out · BRIDGE Medical Leaderboard (H… 64%, SurgiQ: A Large-Scale Multi-D… 28%
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
| source | results | share of the score |
|---|---|---|
 BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab) | 1 | 64% |
 SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models (arXiv:2606.08071v1) | 1 | 28% |
| prior · pull toward the middle | 8% | |
| weighted average | = 38.4 |
$ improve this page
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