whatmodel.sh / Llama3-PBM-Nova-70B
Llama3-PBM-Nova-70B
↗ share
+ contribute
Llama3-PBM-Nova-70B is an open-weights language model from PKU-Baichuan MLSystemLab, released August 2024. Its published size is 70.55B parameters. Licence: llama3.
Catalogue identity notes
Maker repo PKU-Baichuan-MLSystemLab/Llama3-PBM-Nova-70B @1d6d8ee: licence tag llama3. 70,553,706,496 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-08: month the maker's Hugging Face repo was created (createdAt 2024-08-13).
$ how it compares
The LLMs ranked either side of Llama3-PBM-Nova-70B, out of 1132 with a score.
| # | model | score | where |
|---|---|---|---|
| 313 |  GLM-4.7-Flash | 59±1 | api $0.0000/M +local |
| 314 | BitAgent-Bounty-8B | 59±5 | local |
| 315 | Llama3-PBM-Nova-70B | 59±2 | local |
| 316 |  EXAONE 4.0 32B | 59±1 | local |
| 317 |  Phi-4-reasoning-plus 14B | 59±1 | local |
$ what it's good at
chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #163 of 466 * · 99% of its score · 1 test
Beats MiniMax-M2, MiniCPM5 2B, Granite 4.2 30B here, though they rank higher overall. Trails LFM2.5 1.2B Thinking, LFM2.5 8B-A1B, LFM2.5 2.6B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| AlpacaEval 2 length-controlled | 62.4% ✎ | ▁▂▂▃▆▆█▆▄▄▃▁ beats 116/122 | 99% |
$ 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 Llama3-PBM-Nova-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/Llama3-PBM-Nova-70B-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/Llama3-PBM-Nova-70B-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Llama3-PBM-Nova-70B · needs ~42.5 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/Llama3-PBM-Nova-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 Llama3-PBM-Nova-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/Llama3-PBM-Nova-70B-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~141.11 GB · from huggingface.co/PKU-Baichuan-MLSystemLab (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='PKU-Baichuan-MLSystemLab/Llama3-PBM-Nova-70B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/PKU-Baichuan-MLSystemLab ↗
$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%
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 |
|---|---|---|
 AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository) | 1 | 99% |
| prior · pull toward the middle | 1% | |
| weighted average | = 58.8 |
$ 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