Instructions to use brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction with Transformers:

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

pipe = pipeline("text-generation", model="brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction")
model = AutoModelForMultimodalLM.from_pretrained("brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction

SGLang

How to use brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction 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 "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction" \
    --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": "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction",
		"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 "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction" \
        --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": "brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction with Docker Model Runner:
```
docker model run hf.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction
```

NOTE: THIS QUANTIZATION IS BROKEN

See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8-31bpw-exl2-fiction/discussions/4#65a5eb3aee220af178d28541

Yi 34B Merge v8

A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit, quantized with exllamav2 on ~300K tokens of a sci-fi story, a fantasy story, and a vicuna chat for optimal long context storywriting performance.

See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8

Prompt template: Orca-Vicuna

SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:

It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/

Running

24GB GPUs can run 3.1bpw Yi-34B-200K models at 75K context with exllamav2, and performant UIs like exui. I go into more detail in this post

Being a Yi model, try running a lower temperature with 0.05+ MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull the huge vocabulary.

Quantization Commands

First pass:

python /home/alpha/AI/exllamav2/convert.py --in_dir /home/alpha/FastModels/v8/v8 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v8meas.json --cal_dataset /home/alpha/Documents/stories.parquet -ml 32768 -mr 8 -ss 4096 -b 4.0 -hb 6 -nr

Second pass:

python /home/alpha/AI/exllamav2/convert.py --in_dir /home/alpha/FastModels/v8/v8 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v8meas.json --cal_dataset /home/alpha/Documents/stories.parquet -l 12288 -r 26 -ml 32768 -mr 8 -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/v8-exl2-4bpw-fiction -nr

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