whatmodel.sh / internlm2-chat-7b-ExPO
internlm2-chat-7b-ExPO
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internlm2-chat-7b-ExPO is an open-weights language model from Chujie Zheng (ExPO), released May 2024. Its published size is 7.74B parameters. Licence: other.
Catalogue identity notes
Maker repo chujiezheng/internlm2-chat-7b-ExPO @1251176: licence tag other. 7,737,708,544 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-02).
$ how it compares
The LLMs ranked either side of internlm2-chat-7b-ExPO, out of 1132 with a score, then more from Chujie Zheng (ExPO).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 793 |  GPT-3.5 Turbo 16K (0613) | 43±2 | comparison only | ||
| 794 |  Yi 34B Chat | 43±1 | local | ||
| 795 | internlm2-chat-7b-ExPO | 43±2 | local | ||
| 796 |  CogVLM2 Llama3 Chat 19B | 43±3 | local | ||
| 797 |  Code Llama 70B (base) | 43±1 | 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
chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #349 of 466 * · 98% of its score · 1 test
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| AlpacaEval 2 length-controlled | 22.7% ✎ | ▁▂▂▃▆▆█▆▄▄▁▁ beats 58/122 | 98% |
$ 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 internlm2-chat-7b-ExPO · needs ~4.7 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/internlm2-chat-7b-ExPO-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/internlm2-chat-7b-ExPO-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run internlm2-chat-7b-ExPO · needs ~4.7 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/internlm2-chat-7b-ExPO-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 internlm2-chat-7b-ExPO · needs ~4.7 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/internlm2-chat-7b-ExPO-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~15.48 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/internlm2-chat-7b-ExPO', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/chujiezheng ↗
$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 98%
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 | 98% |
| prior · pull toward the middle | 2% | |
| weighted average | = 43.2 |
$ 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