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Dorna-Llama3-8B-Instruct
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Dorna-Llama3-8B-Instruct is an open-weights language model from Part AI, released June 2024. Its published size is 8.03B parameters. Licence: llama3.
Catalogue identity notes
Maker repo PartAI/Dorna-Llama3-8B-Instruct @fb268bb: licence tag llama3. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-01). Gated repository (licence acceptance required on Hugging Face).
$ how it compares
The LLMs ranked either side of Dorna-Llama3-8B-Instruct, out of 1132 with a score, then more from Part AI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 828 |  Llama 3 8B Instruct | 42±0 | local | ||
| 829 |  Hermes 2 Pro Mistral 7B | 42±6 | local | ||
| 830 | Dorna-Llama3-8B-Instruct | 42±5 | local | ||
| 831 |  WizardLM 70B V1.0 | 42±1 | local | ||
| 832 |  WizardCoder Python 13B V1.0 | 42±2 | local | ||
| more from Part AI | |||||
| 790 | Dorna2-Llama3.1-8B-Instruct | 43±5 | local | ||
$ what it's good at
other tests1 not in a task · 90% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open Persian LLM Leaderboard v2 average (21 tasks) | 47.0% ✎ | ▂▄▂▃█▄▃▆▃▅▂▂ beats 22/81 | 90% |
$ 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 Dorna-Llama3-8B-Instruct · needs ~4.9 GB · from huggingface.co/QuantFactorychat in the terminal
llama cli -hf QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Dorna-Llama3-8B-Instruct · needs ~4.9 GB · from huggingface.co/QuantFactory
- Or download it in the terminal
lms get https://huggingface.co/QuantFactory/Dorna-Llama3-8B-Instruct-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 Dorna-Llama3-8B-Instruct · needs ~4.9 GB · from huggingface.co/QuantFactorychat in the terminal
ollama run hf.co/QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~16.06 GB · from huggingface.co/PartAI (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='PartAI/Dorna-Llama3-8B-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/PartAI ↗
$ how the score was worked out · Open Persian LLM Leaderboard… 90%
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 |
|---|---|---|
 Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab) | 1 | 90% |
| prior · pull toward the middle | 10% | |
| weighted average | = 42.0 |
$ 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