whatmodel.sh / Llama-3.1-8B-UltraMedical
Llama-3.1-8B-UltraMedical
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Llama-3.1-8B-UltraMedical is an open-weights language model from Tsinghua C3I, released September 2024. Its published size is 8.03B parameters. Licence: llama3.
Catalogue identity notes
Maker repo TsinghuaC3I/Llama-3.1-8B-UltraMedical @c982b9e: licence tag llama3. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-09: month the maker's Hugging Face repo was created (createdAt 2024-09-01).
$ how it compares
The LLMs ranked either side of Llama-3.1-8B-UltraMedical, out of 1132 with a score, then more from Tsinghua C3I.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 938 |  Gemma 1.1 7B IT | 37±1 | local | ||
| 939 |  Code Llama 7B Python | 37±1 | local | ||
| 940 | Llama-3.1-8B-UltraMedical | 37±5 | local | ||
| 941 |  Dolphin 2.2.1 Mistral 7B | 37±2 | local | ||
| 942 | Code Llama 13B (base) | 37±1 | local | ||
| more from Tsinghua C3I | |||||
| 533 | Llama-3-70B-UltraMedical | 51±5 | local | ||
$ what it's good at
other tests1 not in a task · 89% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BRIDGE clinical text, zero-shot average (87 tasks) | 20.2% ✎ | ▁▁▁▁▃▂▂▃▄▆█▃ beats 9/97 | 89% |
$ 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 Llama-3.1-8B-UltraMedical · needs ~4.9 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/Llama-3.1-8B-UltraMedical-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/Llama-3.1-8B-UltraMedical-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Llama-3.1-8B-UltraMedical · needs ~4.9 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/Llama-3.1-8B-UltraMedical-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 Llama-3.1-8B-UltraMedical · needs ~4.9 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/Llama-3.1-8B-UltraMedical-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~16.06 GB · from huggingface.co/TsinghuaC3I (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='TsinghuaC3I/Llama-3.1-8B-UltraMedical', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/TsinghuaC3I ↗
$ how the score was worked out · BRIDGE Medical Leaderboard (H… 89%
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 | 89% |
| prior · pull toward the middle | 11% | |
| weighted average | = 36.8 |
$ improve this page
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data updated · sources and licences · json