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

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Create README.md

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	---
	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](https://github.com/ggerganov/llama.cpp) commit [fabf30b4c4fca32e116009527180c252919ca922](https://github.com/ggerganov/llama.cpp/commit/fabf30b4c4fca32e116009527180c252919ca922) (master as of 2024-05-20)
	- Imatrix generated with [this](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) 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:

	<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">🐙 GitHub</a> •
	<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
	<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
	<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
	<br/>
	<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
	<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
	<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
	<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 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, 16K, 32K \| 3.6T

	</div>

	# Models

	- Chat models

	<div align="center">

	\| Name \| Download \|
	\| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|

	</div>

	- Base models

	<div align="center">

	\| Name \| Download \|
	\| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|
	\| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\|

	</div>

	# Benchmarks

	- Chat models

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

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png)

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

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png)

	- Base models

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

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png)

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

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png)

	# Quick Start

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