whatmodel.sh / Llama3-OpenBioLLM-70B
Llama3-OpenBioLLM-70B
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Llama3-OpenBioLLM-70B is an open-weights language model from Saama AI Labs, released April 2024. Licence: llama3.
Catalogue identity notes
Maker repo aaditya/Llama3-OpenBioLLM-70B @7ad17ef: licence tag llama3. Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-24).
$ how it compares
The LLMs ranked either side of Llama3-OpenBioLLM-70B, out of 1132 with a score, then more from Saama AI Labs.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 459 |  Olmo 3 7B Think DPO | 54±3 | local | ||
| 460 |  GPT-4o Search Preview (2025-03-11) | 54±5 | api $4.38/M | ||
| 461 | Llama3-OpenBioLLM-70B | 54±3 | local | ||
| 462 | GPT-4 Turbo Preview (0125) | 54±1 | comparison only | ||
| 463 |  Claude 3.5 Haiku | 54±1 | comparison only | ||
| more from Saama AI Labs | |||||
| 869 | Llama3-OpenBioLLM-8B | 40±2 | local | ||
$ what it's good at
other tests2 not in a task · 96% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Polish medical board exams, bias-mitigated MCQ protocol (7-exam average) | 57.2% ✎ | ▄·▂▂▂··█▂▄·▄ beats 8/13 | 54% |
| BRIDGE clinical text, zero-shot average (87 tasks) | 33.0% ✎ | ▁▁▁▁▁▂▂▃▄▆█▃ beats 39/97 | 42% |
$ where to run it
llama.cpp · recommended
- 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 Llama3-OpenBioLLM-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/Llama3-OpenBioLLM-70B-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/Llama3-OpenBioLLM-70B-GGUF:Q4_K_M
LM Studio
- Install LM Studio · Open the app once before using the lms command.
- Run Llama3-OpenBioLLM-70B · needs ~42.5 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/Llama3-OpenBioLLM-70B-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run Llama3-OpenBioLLM-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/Llama3-OpenBioLLM-70B-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~141.11 GB · from huggingface.co/aaditya
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='aaditya/Llama3-OpenBioLLM-70B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Weights and documentation: huggingface.co/aaditya ↗
$ how the score was worked out · Reassessing High-Performing L… 54%, BRIDGE Medical Leaderboard (H… 42%
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 |
|---|---|---|
 Reassessing High-Performing LLMs on Polish Medical Exams: True Competence or Bias-Driven Performance? (arXiv:2606.12250v1) | 1 | 54% |
 BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab) | 1 | 42% |
| prior · pull toward the middle | 4% | |
| weighted average | = 53.8 |
$ 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