[whatmodel.sh](../../README.md) / Jamba 1.5 Mini
# Jamba 1.5 Mini
AI21 Labs · released 2024-08 · 52B params (12B active) · jamba-open-model-license
**score 48.0** ±1.2 · #409 of 732 · from 14 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Jamba 1.5 Mini among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█●▅▅▄▄▂▁ #153 of 313* 3.0% 1
coding ●··········█ #2 of 2* 9.3% 1
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #289 of 402 24% 3
math ▁▁▃▅●▆▄█▄▂▁▁ #328 of 431* 16% 1
agent tasks · #153 of 313 · 3.0% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o4-mini](o4-mini.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL (version not stated) (maker) 80.7% ▄▆▄▂·●▆·▄··▂ beats 8/17 easy · avg 81% 3.0% blunt · small run
coding · #2 of 2 · 9.3% of its score
test result vs the others in its run difficulty counts why
HumanEval (maker) 62.8% ▁▁▁▁▂▂▆▆●▅▂▁ beats 119/205 easy · avg 69% 9.3% sharp · at its level · small run
chat & writing · #289 of 402 · 24% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 8B](ministral-3-8b.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1239.8 ▁▁▁▂▃●▃▆▆▇█▃ beats 88/385 avg 1260 15% blunt
Arena-Hard (version/judge unspeci… 46.1% ▃▃▂●▃▆▅█▅▅▂▃ beats 7/30 medium · avg 55% 4.9% at its level · small run
IFEval (aggregation unspecified) … 75.8% ▁▂▄▂▂●▆██▅▂▁ beats 46/145 easy · avg 79% 3.6% small run
math · #328 of 431 · 16% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Gemma 2 27B IT](gemma-2-27b.md) here, though they rank higher overall. Trails [Stable Code 3B](stable-code-3b.md), [Qwen3 0.6B](qwen3-0-6b.md), [Falcon 40B (base)](falcon-40b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 75.8% ▂▂▂▂▃▄▅▇●█▆▄ beats 130/206 easy · avg 82% 16% sharp · small run
other tests (8)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 53.4% ▁▁▁▁▂▄▅●█▃▃▁ beats 74/155 medium · avg 61% 12% sharp · at its level · small run
MMLU (maker) 69.7% ▁▁▁▁▁▂▂▅█●▆▄ beats 135/214 easy · avg 75% 10% at its level · small run
GPQA (subset unspecified) (maker) 32.3% ▂▁▂▂▄●▆▃▅█▃▃ beats 28/106 very hard · avg 35% 10% sharp · small run
MMLU-Pro (maker) 39.8% ▁▁·▁▁▁●▂▄▅█▁ beats 25/188 medium · avg 46% 7.2% at its level · small run
RULER 64k 92.8% ▁▁·▁·▂▃▄█▂·● beats 22/22 easy · avg 82% 3.4% typical
ARC Challenge (maker) 85.7% ▄▁▁▂▁▄█▇▃▂●▂ beats 134/140 easy · avg 76% 2.5% sharp · at its level · noisy · small run
RULER 128k 90.0% ▄··▂▄▂▆██▄▄● beats 21/21 easy · avg 67% 1.2% at its level · noisy
Vectara hallucination leaderboard… 97.1% ▁▁▂▃▄▄█●▃▂▂▁ beats 106/140 very easy · avg 94% 1.0% blunt · maxed out · noisy
source results share of the score
Jamba-1.5: Hybrid Transformer-Mamba Mod… 10 79% ▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 15% ▇▇
NVIDIA RULER repository README results … 2 5% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 48.0
Licences: Jamba-1.5: Hybrid Transformer-Mamba Models at Scale ([CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Jamba 1.5 Mini (Q4_K_M, needs ~31.2 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/AI21-Jamba-Mini-1.5-GGUF/tree/861a3e7a117f4d9da7cb4e952e37105a007364ce))
```sh
llama cli -hf mradermacher/AI21-Jamba-Mini-1.5-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/AI21-Jamba-Mini-1.5-GGUF:Q4_K_M
```
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Jamba 1.5 Mini (Q4_K_M, needs ~31.2 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/AI21-Jamba-Mini-1.5-GGUF/tree/861a3e7a117f4d9da7cb4e952e37105a007364ce))
```sh
lms get https://huggingface.co/mradermacher/AI21-Jamba-Mini-1.5-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Jamba 1.5 Mini (Q4_K_M, needs ~31.2 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/AI21-Jamba-Mini-1.5-GGUF/tree/861a3e7a117f4d9da7cb4e952e37105a007364ce))
```sh
ollama run hf.co/mradermacher/AI21-Jamba-Mini-1.5-GGUF:Q4_K_M
```
### Other ways to run it
1. Run it as published (BF16, needs ~103.14 GB, NVIDIA only, from [github.com/vllm-project](https://github.com/vllm-project/vllm/blob/64cb68384241f046d07ba3797e1b14dbc82a0b2d/docs/models/supported_models.md))
```sh
vllm serve ai21labs/AI21-Jamba-Mini-1.5
```
Model repository and documentation: [huggingface.co/ai21labs](https://huggingface.co/ai21labs/AI21-Jamba-Mini-1.5)
[interactive version](https://whatmodel.sh/model/jamba-1-5-mini) · [JSON](https://whatmodel.sh/api?format=json&all=1)