[whatmodel.sh](../../README.md) / Llama3-OpenBioLLM-8B
# Llama3-OpenBioLLM-8B
Saama AI Labs · released 2024-04 · llama3
**score 41.0** ±2.3 · #550 of 732 · from 4 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama3-OpenBioLLM-8B among every model on that task or test, worst to best.
other tests (4)
test result vs the others in its run difficulty counts why
MedQA (USMLE, 4 options) 57.8% ▁▁··▄▁▁··▄▃● beats 15/17 medium · avg 71% 47% sharp · at its level
BRIDGE clinical text, zero-shot a… 14.2% ▁▁●▁▁▂▂▃▄▆█▃ beats 2/97 hard · avg 31% 26% blunt
MedMCQA 57.8% ▂▂··▄▄▂▂·▄█● beats 17/17 medium · avg 63% 21% at its level
PubMedQA 72.0% ▂·▂▆·▄▂▄▄●█▂ beats 12/17 medium · avg 75% 2.7% blunt · at its level
source results share of the score
Open Medical-LLM Leaderboard results 3 71% ▇▇▇▇▇▇▇▇▇
BRIDGE Medical Leaderboard 1 26% ▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 41.0
Licences: Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Llama3-OpenBioLLM-8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF/tree/fed5b00257a0b698ae427d9fc520ae49a3c88c37))
```sh
llama cli -hf mradermacher/Llama3-OpenBioLLM-8B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/Llama3-OpenBioLLM-8B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `llama cli -hf mradermacher/Llama3-OpenBioLLM-8B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Llama3-OpenBioLLM-8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF/tree/fed5b00257a0b698ae427d9fc520ae49a3c88c37))
```sh
lms get https://huggingface.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~8.5 GB) `lms get https://huggingface.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF@Q8_0 -y` · 4bit (~5.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Llama3-OpenBioLLM-8B -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Llama3-OpenBioLLM-8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF/tree/fed5b00257a0b698ae427d9fc520ae49a3c88c37))
```sh
ollama run hf.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `ollama run hf.co/mradermacher/Llama3-OpenBioLLM-8B-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Llama3-OpenBioLLM-8B (4bit, needs ~5.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Llama3-OpenBioLLM-8B/tree/0ede5ea7a61964dac781e660cce8706ae091c4ac))
```sh
mlx_lm.chat --model mlx-community/Llama3-OpenBioLLM-8B
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Llama3-OpenBioLLM-8B
```
### Other ways to run it
1. Run it as published (BF16, needs ~16.06 GB, from [huggingface.co/aaditya](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B/tree/70d6bb521cab6ca755b675ade38831eedf89d31c))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='aaditya/Llama3-OpenBioLLM-8B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/aaditya](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
[interactive version](https://whatmodel.sh/model/llama3-openbiollm-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)