[whatmodel.sh](../../README.md) / Zyte-1B
# Zyte-1B
venkycs (aihub-app) · released 2024-01 · 1.1B params · apache-2.0
**score 10.5** ±2.4 · #718 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Zyte-1B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ●▁▁▃▅▇█▇▅▄▃▁ #469 of 469 97% 3
coding · #469 of 469 · 97% of its score
Trails [StableLM-Base-Alpha 7B](stablelm-base-alpha-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 2.4% ●·▁▁▃▄▆▅█▃▁▁ beats 0/99 easy · avg 76% 39% sharp · too hard for it
MBPP+ 7.5% ●···▁▁▂▅▇█▆▂ beats 0/75 easy · avg 66% 30% typical
HumanEval+ 1.8% ●·▁▁▁▃▄▄▄█▃▁ beats 0/99 easy · avg 70% 29% sharp · too hard for it
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 97% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 3% ▇
weighted average = 10.5
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt))
## 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 Zyte-1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Zyte-1B-GGUF/tree/52b6ea94b6c9ea353cc0b45304046763d58ad291))
```sh
llama cli -hf mradermacher/Zyte-1B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/Zyte-1B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.2 GB) `llama cli -hf mradermacher/Zyte-1B-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 Zyte-1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Zyte-1B-GGUF/tree/52b6ea94b6c9ea353cc0b45304046763d58ad291))
```sh
lms get https://huggingface.co/mradermacher/Zyte-1B-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 (~1.2 GB) `lms get https://huggingface.co/mradermacher/Zyte-1B-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 Zyte-1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Zyte-1B-GGUF/tree/52b6ea94b6c9ea353cc0b45304046763d58ad291))
```sh
ollama run hf.co/mradermacher/Zyte-1B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.2 GB) `ollama run hf.co/mradermacher/Zyte-1B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~2.2 GB, from [huggingface.co/venkycs](https://huggingface.co/venkycs/Zyte-1B/tree/ac684856e83b3b19b45d59c71ea6560204b33fff))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='venkycs/Zyte-1B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/venkycs](https://huggingface.co/venkycs/Zyte-1B)
[interactive version](https://whatmodel.sh/model/zyte-1b) · [JSON](https://whatmodel.sh/api?format=json&all=1)