[whatmodel.sh](../../README.md) / StableLM-Tuned-Alpha 7B
# StableLM-Tuned-Alpha 7B
Stability AI · released 2023-04 · 7B params · cc-by-nc-sa-4.0
**score 17.9** ±1.9 · #706 of 732 · from 4 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 StableLM-Tuned-Alpha 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ●▁▁▃▆▄▆█▆▇▇▃ #402 of 402 98% 4
chat & writing · #402 of 402 · 98% of its score
Trails [Gemma 3 270M IT](gemma-3-270m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 953.3 ●▁▁▂▃▃▃▆▆▇█▃ beats 0/385 avg 1260 37% blunt
Polyrating rating (bias-aware re-… 843.0 ●▁▂▁▃▇█▆▇▄▂▂ beats 0/95 avg 1187 34% typical
MT-Bench (GPT-4 single-answer gra… 2.7500 MT-Bench score (1-10) ●▂▂▆▆▄██▂▄▄▆ beats 1/28 avg 8.539 MT-Bench scor… 27% sharp · noisy
MixInstruct GPT-Rank (LLM-Blender… 8.7100 mean rank (1-11) ●···▄··▄···▄ beats 1/4 avg 6.709 mean rank (1-… 0.0% blunt · noisy
source results share of the score
LMArena leaderboard dataset 1 37% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A… 1 34% ▇▇▇▇
LMSYS Chatbot Arena leaderboard Space: … 1 27% ▇▇▇
LLM-Blender: Ensembling Large Language … 1 0% ▇
prior: pull toward the middle 2% ▇
weighted average = 17.9
Licences: 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/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion ([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 StableLM-Tuned-Alpha 7B (Q4_K_M, needs ~5.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-GGUF/tree/d5193c735c9c53872ee34f1fef01140922fa5a71))
```sh
llama cli -hf mradermacher/stablelm-tuned-alpha-7b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/stablelm-tuned-alpha-7b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.4 GB) `llama cli -hf mradermacher/stablelm-tuned-alpha-7b-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 StableLM-Tuned-Alpha 7B (Q4_K_M, needs ~5.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-GGUF/tree/d5193c735c9c53872ee34f1fef01140922fa5a71))
```sh
lms get https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-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 (~8.4 GB) `lms get https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-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 StableLM-Tuned-Alpha 7B (Q4_K_M, needs ~5.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-GGUF/tree/d5193c735c9c53872ee34f1fef01140922fa5a71))
```sh
ollama run hf.co/mradermacher/stablelm-tuned-alpha-7b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.4 GB) `ollama run hf.co/mradermacher/stablelm-tuned-alpha-7b-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~31.74 GB, from [huggingface.co/stabilityai](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b/tree/25071b093c15c0d1cb2b2876c6deb621b764fcf5))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='stabilityai/stablelm-tuned-alpha-7b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/stabilityai](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b)
[interactive version](https://whatmodel.sh/model/stablelm-tuned-alpha-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)