[whatmodel.sh](../../README.md) / SOLAR 10.7B Instruct v1.0
# SOLAR 10.7B Instruct v1.0
Upstage · released 2023-12 · 10.7B params · cc-by-nc-4.0
**score 39.2** ±0.7 · #575 of 732 · from 13 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 SOLAR 10.7B Instruct v1.0 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #409 of 469 75% 3
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #356 of 402* 10% 2
coding · #409 of 469 · 75% 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 43.3% ▁·▁▁▃●▆▅█▃▁▁ beats 27/99 easy · avg 76% 39% sharp · at its level
HumanEval+ 37.2% ▁·▁▁▁▃●▄▄█▃▁ beats 26/99 easy · avg 70% 30% sharp · at its level
MBPP+ 36.2% ▁···▁●▂▅▇█▆▂ beats 4/75 easy · avg 66% 6.0% at its level
chat & writing · #356 of 402 · 10% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-4k-instruct (June 2024 update)](phi-3-mini-4k-instruct-june-2024.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 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1152.5 ▁▁▁▂●▃▃▆▆▇█▃ beats 42/385 avg 1260 5.1% blunt
Polyrating rating (bias-aware re-… 1064.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 31/95 avg 1187 4.7% typical
other tests (8)
test result vs the others in its run difficulty counts why
CPTUB average (SpeakLeash, 0-5 ju… 57.6% ▁▂▂▂▃●▃▆▅▄▂▃ beats 24/95 medium · avg 65% 4.2% at its level
MedQA (USMLE, 4 options) 54.7% ▁▁··▄▁▁··▄▃● beats 13/17 medium · avg 71% 4.0% sharp · at its level
Polish EQ-Bench v2 (SpeakLeash) 57.4% ▁··▁▁▂▂▆▅●█▆ beats 35/70 medium · avg 62% 3.0% at its level
MedMCQA 47.4% ▂▂··▄▄▂▂·▄●█ beats 10/17 medium · avg 63% 1.8% at its level
Open PT-LLM Leaderboard average (… 71.4% ▃▁▁▁▁▁▁▁▂●▅▁ beats 184/276 medium · avg 72% 1.1% at its level · noisy
TriviaQA 42.0% ▁··▁█▃▃▁▄●▄█ beats 17/28 easy · avg 73% 0.6% at its level · noisy
PubMedQA 52.6% ●·▂▆·▄▂▄▄▂█▂ beats 0/17 medium · avg 75% 0.2% blunt · at its level
TruthfulQA MC2 71.7% ▅▃█▆▁▂▅···▁● beats 31/31 medium · avg 57% 0.1% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 75% ▇▇▇▇▇▇▇▇▇
Open Medical-LLM Leaderboard results 3 6% ▇
LMArena leaderboard dataset 1 5% ▇
Polyrating: A Cost-Effective and Bias-A… 1 5% ▇
SpeakLeash CPTUB 1 4% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Hallucinations Leaderboard results 2 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 39.2
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · 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/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Hallucinations Leaderboard results ([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 SOLAR 10.7B Instruct v1.0 (Q4_K_M, needs ~6.5 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-GGUF/tree/2d2498cc4fcc3b93667fc5bb191214a7e476cfed))
```sh
llama cli -hf mradermacher/SOLAR-10.7B-Instruct-v1.0-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/SOLAR-10.7B-Instruct-v1.0-GGUF:Q4_K_M
```
Other versions: Q8_0 (~11.4 GB) `llama cli -hf mradermacher/SOLAR-10.7B-Instruct-v1.0-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 SOLAR 10.7B Instruct v1.0 (Q4_K_M, needs ~6.5 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-GGUF/tree/2d2498cc4fcc3b93667fc5bb191214a7e476cfed))
```sh
lms get https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-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 (~11.4 GB) `lms get https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-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 SOLAR 10.7B Instruct v1.0 (Q4_K_M, needs ~6.92 GB, from [ollama.com](https://ollama.com/library/solar/tags))
```sh
ollama run solar:10.7b-instruct-v1-q4_K_M
```
Other versions: Q8_0 (~13.34 GB) `ollama run solar:10.7b-instruct-v1-q8_0`
Model repository and documentation: [huggingface.co/upstage](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
[interactive version](https://whatmodel.sh/model/solar-10-7b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)