whatmodel.sh / CoALM-70B
CoALM-70B
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CoALM-70B is an open-weights language model from UIUC ConvAI & Oumi (CoALM), released February 2025. Its published size is 70.55B parameters. Licence: cc-by-nc-4.0.
Catalogue identity notes
Maker repo uiuc-convai/CoALM-70B @c2bcfc0: licence tag cc-by-nc-4.0. 70,553,706,496 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-02: month the maker's Hugging Face repo was created (createdAt 2025-02-03).
$ how it compares
The LLMs ranked either side of CoALM-70B, out of 1132 with a score, then more from UIUC ConvAI & Oumi (CoALM).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 604 |  LFM2.5 1.2B Thinking | 49±2 | local | ||
| 605 |  Qwen3-VL 2B Thinking | 49±1 | local | ||
| 606 | CoALM-70B | 49±4 | local | ||
| 607 |  speechless-codellama-34b-v2.0 | 49±1 | local | ||
| 608 |  Pixtral 12B (2409) | 49±2 | local | ||
| more from UIUC ConvAI & Oumi (CoALM) | |||||
| 308 | CoALM-405B | 59±7 | local | ||
| 627 | CoALM-8B | 48±4 | local | ||
$ what it's good at
agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #262 of 334 * · 93% of its score · 2 tests
Beats o3-mini, Ministral 3 14B Reasoning 2512, DeepSeek R1 0528 Qwen3 8B here, though they rank higher overall. Trails Granite 4.0 1B, SmolLM3 3B, Phi-3.5-mini-instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| BFCL V4 (December 2025 archive) | 28.0% ✎ | ▁▁▁▂██▅▅▆▂▂▂ beats 25/77 | 61% |
| BFCL v3 | 54.5% ✎ | ▁▁▁▁▁▃▅█▅▄▁▁ beats 53/114 | 32% |
$ 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 CoALM-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/CoALM-70B-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/CoALM-70B-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run CoALM-70B · needs ~42.5 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/CoALM-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 CoALM-70B · needs ~42.5 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/CoALM-70B-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~141.11 GB · from huggingface.co/uiuc-convai (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='uiuc-convai/CoALM-70B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/uiuc-convai ↗
$ how the score was worked out · Berkeley Function Calling Lea… 93%
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 |
|---|---|---|
 Berkeley Function Calling Leaderboard result archive | 2 | 93% |
| prior · pull toward the middle | 7% | |
| weighted average | = 48.9 |
$ 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