[whatmodel.sh](../../README.md) / Gemma 1.1 7B IT # Gemma 1.1 7B IT Google · released 2024-04 · 8.54B params · gemma **score 38.7** ±0.7 · #581 of 732 · from 12 benchmark results across 8 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Gemma 1.1 7B IT among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
coding          ▁▁▁▃●▇█▇▅▄▃▁   #411 of 469     68%      3
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #331 of 402     13%      3
coding · #411 of 469 · 68% of its score Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [Code Llama 7B Instruct](codellama-7b-instruct.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall. 
test        result  vs the others in its run  difficulty      counts  why
HumanEval    42.7%  ▁·▁▁▃●▆▅█▃▁▁ beats 26/99  easy · avg 76%     36%  sharp · at its level
HumanEval+   35.4%  ▁·▁▁▁▃●▄▄█▃▁ beats 24/99  easy · avg 70%     27%  sharp · at its level
MBPP+        45.0%  ▁···▁▁▂●▇█▆▂ beats 10/75  easy · avg 66%    5.5%  at its level
chat & writing · #331 of 402 · 13% of its score Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
LMArena (text)                      1182.6  ▁▁▁▂●▃▃▆▆▇█▃ beats 57/385  avg 1260            4.7%  blunt
Polyrating rating (bias-aware re-…  1097.0  ▂▁▂▁▃▇●▆▇▄▂▂ beats 43/95   avg 1187            4.3%  typical
Arena-Hard (v0.1, GPT-4-1106 judg…   12.1%  ▂▁▁●█▅▄▃▃▅▃▁ beats 6/60    medium · avg 47%    4.3%  sharp
other tests (6) 
test                                result  vs the others in its run   difficulty           counts  why
LiveBench 2024-07-26                 18.8%  ▁▁▁▂▂●▂█▆▃▅▃ beats 14/61   medium · avg 38%        12%  typical
MedQA (USMLE, 4 options)             45.7%  ▁▁··▄▁▁··●▃█ beats 9/17    medium · avg 71%       3.6%  sharp · at its level
MedMCQA                              44.4%  ▂▂··▄▄▂▂·●██ beats 8/17    medium · avg 63%       1.6%  at its level
Open PT-LLM Leaderboard average (…   53.9%  ▃▁▁▁▁▁▁●▂█▅▁ beats 73/276  medium · avg 72%       1.0%  at its level · noisy
Vectara hallucination leaderboard…   83.0%  ▁▁●▃▄▄█▆▃▂▂▁ beats 8/140   very easy · avg 94%    0.5%  blunt · noisy
PubMedQA                             70.8%  ▂·▂▆·▄▂▄●▂█▂ beats 10/17   medium · avg 75%       0.2%  blunt · at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        3   68% ▇▇▇▇▇▇▇▇
LiveBench model judgments                       1   12% ▇
Open Medical-LLM Leaderboard results            3    5% ▇
LMArena leaderboard dataset                     1    5% ▇
Polyrating: A Cost-Effective and Bias-A…        1    4% ▇
Arena-Hard-Auto v0.1 leaderboard                1    4% ▇
Open Portuguese LLM Leaderboard                 1    1% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 38.7
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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 Gemma 1.1 7B IT (Q4_K_M, needs ~5.3 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/tree/ca2bc3fc8e393fa6b7ebcd580c6f4fe833561017)) ```sh llama cli -hf ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf ggml-org/gemma-1.1-7b-it-Q4_K_M-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 Gemma 1.1 7B IT (Q4_K_M, needs ~5.3 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/tree/ca2bc3fc8e393fa6b7ebcd580c6f4fe833561017)) ```sh lms get https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 4bit (~5.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/gemma-1.1-7b-it-4bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Gemma 1.1 7B IT (Q4_K_M, needs ~5.62 GB, from [ollama.com](https://ollama.com/library/gemma/tags)) ```sh ollama run gemma:7b-instruct-v1.1-q4_K_M ``` Other versions: Q8_0 (~10.75 GB) `ollama run gemma:7b-instruct-v1.1-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Gemma 1.1 7B IT (4bit, needs ~5.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/gemma-1.1-7b-it-4bit/tree/1ece0a8afa79947b51ba9c98bed6b403bdccab88)) ```sh mlx_lm.chat --model mlx-community/gemma-1.1-7b-it-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/gemma-1.1-7b-it-4bit ``` Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-1.1-7b-it) [interactive version](https://whatmodel.sh/model/gemma-1-1-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)