Instructions to use afrideva/Yi-1.5-6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use afrideva/Yi-1.5-6B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/Yi-1.5-6B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/afrideva/Yi-1.5-6B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use afrideva/Yi-1.5-6B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "afrideva/Yi-1.5-6B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "afrideva/Yi-1.5-6B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/afrideva/Yi-1.5-6B-GGUF:Q4_K_M
- Ollama
How to use afrideva/Yi-1.5-6B-GGUF with Ollama:
ollama run hf.co/afrideva/Yi-1.5-6B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use afrideva/Yi-1.5-6B-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/Yi-1.5-6B-GGUF:Q4_K_M
- Lemonade
How to use afrideva/Yi-1.5-6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/Yi-1.5-6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Yi-1.5-6B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 4,641 Bytes
40756aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | ---
base_model: 01-ai/Yi-1.5-6B
inference: true
license: apache-2.0
model_creator: 01-ai
model_name: Yi-1.5-6B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
---
# Yi-1.5-6B-GGUF
Quantized GGUF model files for [Yi-1.5-6B](https://huggingface.co/01-ai/Yi-1.5-6B) from [01-ai](https://huggingface.co/01-ai)
## Original Model Card:
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">π GitHub</a> β’
<a href="https://discord.gg/hYUwWddeAu">πΎ Discord</a> β’
<a href="https://twitter.com/01ai_yi">π€ Twitter</a> β’
<a href="https://github.com/01-ai/Yi-1.5/issues/2">π¬ WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">π Paper</a> β’
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">π FAQ</a> β’
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">π Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | β’ [π€ Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) β’ [π€ ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.

Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.

- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.

Yi-1.5-9B is the top performer among similarly sized open-source models.

# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5). |