[whatmodel.sh](../../README.md) / Llama 2 7B Chat
# Llama 2 7B Chat
Meta · released 2023-07 · 6.74B params · llama2
**score 32.0** ±0.9 · #650 of 732 · from 30 benchmark results across 19 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 2 7B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁●▁▃▅▇█▇▅▄▃▁ #462 of 469* 4.9% 1
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #386 of 402 35% 7
math ▁▁▃●█▆▄█▄▂▁▁ #370 of 431* 3.7% 1
coding · #462 of 469 · 4.9% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md) here, though they rank higher overall. Trails [Phi-1 (1.3B)](phi-1.md), [Pythia 6.9B](pythia-6-9b.md), [GPT-J 6B](gpt-j-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Tulu 2: CodexEval pass@10 24.0% ●▃▃·▆▃▃█▃▂▂▅ beats 0/25 easy · avg 83% 4.9% sharp · at its level
chat & writing · #386 of 402 · 35% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [Nous-Hermes-13b](nous-hermes-13b.md), [Qwen1.5 4B Chat](qwen1-5-4b-chat.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 5.3% ▁▂●▃▆▆█▆▄▄▁▁ beats 8/122 medium · avg 35% 8.2% sharp · too hard for it
LMArena (text) 1108.0 ▁▁▁●▃▃▃▆▆▇█▃ beats 16/385 avg 1260 7.8% blunt
Polyrating rating (bias-aware re-… 1036.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 21/95 avg 1187 7.1% typical
MT-Bench (GPT-4 single-answer gra… 6.2700 MT-Bench score (1-10) ▆▂▂▆▆▄●█▂▄▄▆ beats 14/28 avg 8.539 MT-Bench scor… 5.5% sharp · noisy
Arena-Hard (v0.1, GPT-4-1106 judg… 4.6% ●▁▁▅█▅▄▃▃▅▃▁ beats 2/60 medium · avg 47% 2.4% sharp · too hard for it
AlpacaEval 1.0 win rate (vs text-… 71.4% ▂▂·▆·●▃▆█▆▄▂ beats 16/45 very easy · avg 94% 2.1% sharp · at its level · noisy
Arabic IFEval prompt-level (Incep… 0.7% ●·▁▁▂▁▄▄▆▄█▄ beats 0/38 medium · avg 46% 1.9% sharp · too hard for it
math · #370 of 431 · 3.7% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 12.0% ●▂▅█▅▅▃▆···▂ beats 3/25 easy · avg 72% 3.7% sharp
other tests (21)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 11.6% ▁▁▁●▂▃▂█▆▃▅▃ beats 6/61 medium · avg 38% 13% typical
WildBench v2 WB-Score (task macro) 8.2621 WB-Score ▁·▁●·▃█▄▇▆▅▆ beats 2/52 avg 43.019 WB-Score 8.3% sharp
BIG-Bench Hard 25.6% ●·····▂█▂▅·▁ beats 0/25 easy · avg 74% 6.5% sharp
Natural Questions (closed book) 23.7% ▁·▁·▁▂▁▂·▅●█ beats 18/28 medium · avg 48% 5.9% typical
Open Persian LLM Leaderboard v2 a… 31.7% ●▄▂▃█▄▃▆▃▅▂▂ beats 2/81 medium · avg 57% 3.3% blunt · at its level
MMLU 46.8% ▁▁▃▃●█▄▁█··▁ beats 7/25 medium · avg 73% 3.2% typical
BRIDGE clinical text, zero-shot a… 16.5% ▁▁▁●▁▂▂▃▄▆█▃ beats 6/97 hard · avg 31% 2.8% blunt
VEC embodied concepts, zero-shot … 56.7% ▁····▁▁·▁·▃● beats 11/11 hard · avg 63% 2.8% typical
EQ-Bench v1 (60 questions, paper … 25.4% ▂··●·▂▆▄▂█·▂ beats 2/14 medium · avg 53% 2.6% at its level
HaluEval knowledge-grounded dialo… 44.0% ●·········▄▄ beats 0/3 medium · avg 68% 1.5% sharp · too hard for it · noisy
Open PT-LLM Leaderboard average (… 52.2% ▃▁▁▁▁▁▁●▂█▅▁ beats 72/276 medium · avg 72% 1.3% noisy
EuroEval Life in the UK (English … 54.3% ▁▁▁▁▁▁▁●▄█▄▃ beats 25/146 easy · avg 82% 1.2% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 22.7% ●··▁▃▂▂▂█▅▁▂ beats 0/25 medium · avg 59% 1.1% blunt · at its level
Vectara hallucination leaderboard… 88.7% ▁▁▂●▄▄█▆▃▂▂▁ beats 19/140 very easy · avg 94% 1.1% blunt · noisy
TriviaQA 8.8% ▁··▁●▃▃▁▄▅▄█ beats 3/28 easy · avg 73% 0.9% at its level · noisy
HaluEval QA hallucination detecti… 52.3% █▁▁·▁●·▁▁▁▁▁ beats 20/27 hard · avg 66% 0.4% too hard for it · noisy
HaluEval QA hallucination recogni… 49.6% ●··········▃ beats 0/3 very hard · avg 56% 0.3% too hard for it · noisy
TruthfulQA MC2 45.3% ▅▃█●▁▂▅···▁▁ beats 20/31 medium · avg 57% 0.2% blunt · at its level
VEC visual concepts, zero-shot pe… 63.8% ▅▅▃●·▃··▃·▃▃ beats 7/11 hard · avg 66% 0.0% blunt · at its level
Chess Puzzles (Epoch) 0.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 8/137 very hard · avg 5.8% 0.0% sharp · too hard for it · noisy
HaluEval summarization hallucinat… 49.5% ●··········▃ beats 0/3 very hard · avg 50% 0.0% blunt · too hard for it · noisy
source results share of the score
Tulu 2 5 19% ▇▇
LiveBench model judgments 1 13% ▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 10% ▇
WildBench v2 leaderboard data 1 8% ▇
LMArena leaderboard dataset 1 8% ▇
Hallucinations Leaderboard results 4 7% ▇
Polyrating: A Cost-Effective and Bias-A… 1 7% ▇
LMSYS Chatbot Arena leaderboard Space: … 1 6% ▇
Open Persian LLM Leaderboard v2 1 3% ▇
BRIDGE Medical Leaderboard 1 3% ▇
Can Language Models Understand Physical… 2 3% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 3% ▇
Arena-Hard-Auto v0.1 leaderboard 1 2% ▇
Inception Arabic/Hindi leaderboards 1 2% ▇
HaluEval: A Large-Scale Hallucination E… 3 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
EuroEval leaderboards 1 1% ▇
Vectara hallucination leaderboard 1 1% ▇
Epoch AI 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 32.0
Licences: Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Can Language Models Understand Physical Concepts? ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-726-CC-BY-4-0.txt)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-397-CC-BY-4-0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 Llama 2 7B Chat (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/191239b3e26b2882fb562ffccdd1cf0f65402adb))
```sh
llama cli -hf TheBloke/Llama-2-7B-Chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Llama-2-7B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/Llama-2-7B-Chat-GGUF:Q8_0`
### 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 Llama 2 7B Chat (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/191239b3e26b2882fb562ffccdd1cf0f65402adb))
```sh
lms get https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.2 GB) `lms get https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF@Q8_0 -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 Llama 2 7B Chat (Q4_0, needs ~4.54 GB, from [ollama.com](https://ollama.com/library/llama2/tags))
```sh
ollama run llama2:7b-chat
```
Other versions: Q8_0 (~8.59 GB) `ollama run llama2:7b-chat-q8_0`
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
[interactive version](https://whatmodel.sh/model/llama-2-7b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)