Instructions to use yuuko-eth/Chihiro-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuuko-eth/Chihiro-7B-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yuuko-eth/Chihiro-7B-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuuko-eth/Chihiro-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("yuuko-eth/Chihiro-7B-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuuko-eth/Chihiro-7B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuuko-eth/Chihiro-7B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuuko-eth/Chihiro-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yuuko-eth/Chihiro-7B-v0.1

SGLang

How to use yuuko-eth/Chihiro-7B-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yuuko-eth/Chihiro-7B-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuuko-eth/Chihiro-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yuuko-eth/Chihiro-7B-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuuko-eth/Chihiro-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yuuko-eth/Chihiro-7B-v0.1 with Docker Model Runner:
```
docker model run hf.co/yuuko-eth/Chihiro-7B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

千尋 7B v0.1

Zebrafish 7B 加上 Breeze 7B 的 slerp merge 試驗性通用繁中基座模型 📚

GGUF Quants 👉 Chihiro-7B-v0.1-GGUF

請用 Mistral 7B Instruct 或是 Breeze 7B Instruct 所推薦的 Prompt 格式進行操作；以下為模型配置。

Chihiro 7B v0.1

This is an experimental Mistral-architecture SLERP merge with two brilliant base models. Zebrafish and Breeze were used together in this work.

Model configuration is as follows:

Breeze-7B-Instruct as base.
Zebrafish-7B as model 1.

To use the model, please use either prompt templates suggested by the base models, or just slap the Mistral one on.

Benchmarks

Evaluation suite: OpenLLM

Model	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Chihiro-7B-v0.1	68.52	85.95	(not yet evaluated)	63.81	81.77	64.22

Evaluation suite: Nous

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Chihiro-7B-v0.1	45.16	75.26	63.82	47.38	57.91

Average: 47.38%

Average score: 57.91%

Evaluated Apr. 27, 2024, NVIDIA RTX 4090

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for yuuko-eth/Chihiro-7B-v0.1

Quantizations

2 models