Instructions to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF", filename="Peach-2.0-9B-8k-Roleplay.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-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 QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-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 QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Peach-2.0-9B-8k-Roleplay-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": "QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with Ollama:
ollama run hf.co/QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Peach-2.0-9B-8k-Roleplay-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF
This is quantized version of ClosedCharacter/Peach-2.0-9B-8k-Roleplay created using llama.cpp
Original Model Card
[ English | 中文 ]
Peach-2.0-9B-8k-Roleplay
Peach-2.0-9B-8k-Roleplay is a chat large language model obtained by finetuning 01-ai/Yi-1.5-9B model on more than 100K conversations created through our data synthesis approach.
Thanks For FlowGPT's support.
Maybe The Best LLM with Small Parameters under 34B
What's New
Finally, after much anticipation, Peach_v2.0 has been open-sourced!
We completed the final SFT+DPO training in early January, followed by extensive testing, before concluding that this version meets the standards for a commercial release, now freely available to everyone. Our goal is to break the paid monopoly, allowing everyone to have their own local role-playing AI!
Compared to the previous version, we've made the following improvements:
- Compatibility with Silly-Tavern output format, making it easy for every role-playing enthusiast to quickly get started and use!
- Enhanced the model's writing capabilities, achieving a significant leap in plot output and action description!
- Strengthened the model's bilingual capabilities, requiring only two lines of prompt to perfectly accommodate English character cards in Chinese conversations!
- Improved the model's interaction abilities, giving it higher intelligence & emotional quotient in terms of plot progression and topic weaving!
- Introduced a DPO training phase for preference alignment, addressing issues such as context repetition and logical errors in the SFT model, resulting in superior dialogue performance.
Enjoy~
How to start
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
prefix = "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.\n"
suffix = "\n\nYou must response in Chinese."
model_name_or_path = "ClosedCharacter/Peach-2.0-9B-8k-Roleplay"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path, torch_dtype=torch.bfloat16,
trust_remote_code=True, device_map="auto")
system_prompt = "You are Harry Potter"
# If you want to chat in Chinese, just add prefix and suffix like below:
# system_prompt = prefix + system_prompt + suffix
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "Hello"},
{"role": "character", "content": "Hi"},
{"role": "user", "content": "Who are you?"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors="pt")
output = model.generate(
inputs=input_ids.to("cuda"),
temperature=0.5,
top_p=0.7,
repetition_penalty=1.05,
eos_token_id=7,
max_new_tokens=512)
print(tokenizer.decode(output[0]))
Or you can just use below code to run web demo.
python demo.py
Warning
All response are generated by AI and do not represent the views or opinions of the developers.
Despite having done rigorous filtering, due to the uncontrollability of LLM, our model may still generate toxic, harmful, and NSFW content.
Due to limitations in model parameters, the 9B model may perform poorly on mathematical tasks, coding tasks, and logical capabilities.
Our training data is capped at a maximum length of 8k, so excessively long conversation turns may result in a decline in the quality of responses.
We used bilingual Chinese-English data for training, so the model may not perform well on other low-resource languages.
The model may generate a significant amount of hallucinations, so it is recommended to use lower values for temperature and top_p parameters.
Contact Us
微信 / WeChat: Fungorum
邮箱 / E-mail: 1070193753@qq.com
Thanks For FlowGPT's support, which is a dynamic tool that harnesses the power of AI to streamline various creative and professional tasks.
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