whatmodel.sh / MAP-Neo 7B Instruct v0.1 ExPO
MAP-Neo 7B Instruct v0.1 ExPO
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MAP-Neo 7B Instruct v0.1 ExPO is an open-weights language model from Chujie Zheng (ExPO), released May 2024. Its published size is 7.79B parameters. Licence: apache-2.0.
Catalogue identity notes
Maker repo chujiezheng/neo_7b_instruct_v0.1-ExPO @2b42768: licence tag apache-2.0. 7,793,716,224 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-30).
$ how it compares
The LLMs ranked either side of MAP-Neo 7B Instruct v0.1 ExPO, out of 1132 with a score, then more from Chujie Zheng (ExPO).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 922 | zephyr-7b-beta-ExPO | 38±2 | local | ||
| 923 |  Qwen3.5 0.8B | 38±1 | local | ||
| 924 | MAP-Neo 7B Instruct v0.1 ExPO | 38±3 | local | ||
| 925 |  Sarvam-M | 38±5 | local | ||
| 926 |  Code Tulu 2 7B | 38±2 | local | ||
| more from Chujie Zheng (ExPO) | |||||
| 548 | Llama-3-Instruct-8B-SimPO-ExPO | 51±1 | local | ||
| 660 | Mistral7B-PairRM-SPPO-ExPO | 47±2 | local | ||
| 730 | internlm2-chat-20b-ExPO | 45±2 | local | ||
$ what it's good at
other tests1 not in a task · 96% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| WildBench v2 WB-Score (task macro) | 23.1142 WB-Score ✎ | ▁·▁▁·▃█▄▇▆▅▆ beats 4/52 | 96% |
$ where to run it
Other ways to run it
- Run it as published · needs ~15.59 GB · from huggingface.co/chujiezheng (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='chujiezheng/neo_7b_instruct_v0.1-ExPO', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/chujiezheng ↗
$ how the score was worked out · WildBench v2 leaderboard data… 96%
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 |
|---|---|---|
 WildBench v2 leaderboard data (Ai2) | 1 | 96% |
| prior · pull toward the middle | 4% | |
| weighted average | = 37.7 |
$ 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