Instructions to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-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 qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-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 qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-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 qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-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 qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M

Ollama
How to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF with Ollama:
```
ollama run hf.co/qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M
```

Lemonade

How to use qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qwp4w3hyb/Yi-1.5-34B-Chat-16K-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Yi-1.5-34B-Chat-16K-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Yi-1.5-34B-Chat-16K-iMat-GGUF / README.md

qwp4w3hyb

Create README.md

0be659e verified over 2 years ago

preview code

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metadata

license: apache-2.0
pipeline_tag: text-generation
base_model: 01-ai/Yi-1.5-34B-Chat-16K
tags:
  - yi
  - 01-ai
  - instruct
  - finetune
  - chatml
  - gguf
  - imatrix
  - importance matrix
model-index:
  - name: Yi-1.5-34B-Chat-16K-iMat-GGUF
    results: []

Quant Infos

quants done with an importance matrix for improved quantization loss
ggufs & imatrix generated from bf16 for "optimal" accuracy loss
Wide coverage of different gguf quant types from Q_8_0 down to IQ1_S
Quantized with llama.cpp commit fabf30b4c4fca32e116009527180c252919ca922 (master as of 2024-05-20)

Imatrix generated with this multi-purpose dataset.

./imatrix -c 512 -m $model_name-f16.gguf -f $llama_cpp_path/groups_merged.txt -o $out_path/imat-f16-gmerged.dat

Original Model Card:

🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat
📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 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	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-34B-Chat-16K	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-9B-Chat	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-9B-Chat-16K	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-6B-Chat	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel

Base models

Name	Download
Yi-1.5-34B	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-34B-32K	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-9B	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-9B-32K	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 wisemodel
Yi-1.5-6B	• 🤗 Hugging Face • 🤖 ModelScope • 🔍 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.