Instructions to use RichardErkhov/01-ai_-_Yi-1.5-9B-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 RichardErkhov/01-ai_-_Yi-1.5-9B-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 RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/01-ai_-_Yi-1.5-9B-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 RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/01-ai_-_Yi-1.5-9B-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 RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/01-ai_-_Yi-1.5-9B-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 RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/01-ai_-_Yi-1.5-9B-gguf with Ollama:
ollama run hf.co/RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/01-ai_-_Yi-1.5-9B-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/01-ai_-_Yi-1.5-9B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/01-ai_-_Yi-1.5-9B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.01-ai_-_Yi-1.5-9B-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
Yi-1.5-9B - GGUF
- Model creator: https://huggingface.co/01-ai/
- Original model: https://huggingface.co/01-ai/Yi-1.5-9B/
| Name | Quant method | Size |
|---|---|---|
| Yi-1.5-9B.Q2_K.gguf | Q2_K | 3.12GB |
| Yi-1.5-9B.IQ3_XS.gguf | IQ3_XS | 3.46GB |
| Yi-1.5-9B.IQ3_S.gguf | IQ3_S | 3.64GB |
| Yi-1.5-9B.Q3_K_S.gguf | Q3_K_S | 3.63GB |
| Yi-1.5-9B.IQ3_M.gguf | IQ3_M | 3.78GB |
| Yi-1.5-9B.Q3_K.gguf | Q3_K | 4.03GB |
| Yi-1.5-9B.Q3_K_M.gguf | Q3_K_M | 4.03GB |
| Yi-1.5-9B.Q3_K_L.gguf | Q3_K_L | 4.37GB |
| Yi-1.5-9B.IQ4_XS.gguf | IQ4_XS | 4.5GB |
| Yi-1.5-9B.Q4_0.gguf | Q4_0 | 4.69GB |
| Yi-1.5-9B.IQ4_NL.gguf | IQ4_NL | 4.73GB |
| Yi-1.5-9B.Q4_K_S.gguf | Q4_K_S | 4.72GB |
| Yi-1.5-9B.Q4_K.gguf | Q4_K | 4.96GB |
| Yi-1.5-9B.Q4_K_M.gguf | Q4_K_M | 4.96GB |
| Yi-1.5-9B.Q4_1.gguf | Q4_1 | 5.19GB |
| Yi-1.5-9B.Q5_0.gguf | Q5_0 | 5.69GB |
| Yi-1.5-9B.Q5_K_S.gguf | Q5_K_S | 5.69GB |
| Yi-1.5-9B.Q5_K.gguf | Q5_K | 5.83GB |
| Yi-1.5-9B.Q5_K_M.gguf | Q5_K_M | 5.83GB |
| Yi-1.5-9B.Q5_1.gguf | Q5_1 | 6.19GB |
| Yi-1.5-9B.Q6_K.gguf | Q6_K | 6.75GB |
| Yi-1.5-9B.Q8_0.gguf | Q8_0 | 8.74GB |
Original model description:
license: apache-2.0
π GitHub β’
πΎ Discord β’
π€ Twitter β’
π¬ WeChat
π Paper β’
πͺ Tech Blog β’
π FAQ β’
π Learning Hub
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.
| Model | Context Length | Pre-trained Tokens |
|---|---|---|
| Yi-1.5 | 4K, 16K, 32K | 3.6T |
Models
Chat models
Name Download Yi-1.5-34B-Chat β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-34B-Chat-16K β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-9B-Chat β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-9B-Chat-16K β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-6B-Chat β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Base models
Name Download Yi-1.5-34B β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-34B-32K β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-9B β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-9B-32K β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel Yi-1.5-6B β’ π€ Hugging Face β’ π€ ModelScope β’ π£ wisemodel
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.
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