Instructions to use brucethemoose/Yi-34B-200K-RPMerge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brucethemoose/Yi-34B-200K-RPMerge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="brucethemoose/Yi-34B-200K-RPMerge")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("brucethemoose/Yi-34B-200K-RPMerge") model = AutoModelForCausalLM.from_pretrained("brucethemoose/Yi-34B-200K-RPMerge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brucethemoose/Yi-34B-200K-RPMerge 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-RPMerge" # 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-RPMerge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/brucethemoose/Yi-34B-200K-RPMerge
- SGLang
How to use brucethemoose/Yi-34B-200K-RPMerge 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-RPMerge" \ --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-RPMerge", "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-RPMerge" \ --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-RPMerge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use brucethemoose/Yi-34B-200K-RPMerge with Docker Model Runner:
docker model run hf.co/brucethemoose/Yi-34B-200K-RPMerge
Suggestion: increase the weights of instruction-following models.
First thank you for this already good merge.
I appreciate the idea of dropping the bagel because its inherent problem of ppl at long ctx. However, I find the resulting model does not follow instructions very well. For example, in a role playing session, I often use direct prompts (/sys in SillyTavern) instead of acting as my character to direct the story. And this model often ignores there prompts and continue as is. I presume it's a bit lacking in instruction following capability as the backbone of this merge are mostly non-instruct tuned base models/"undertrained" ones.
The bagel had a new version of 0.4 now: https://huggingface.co/jondurbin/bagel-34b-v0.4 Probably you could add it back at a lower weight (or wait for the 0.4 dpo version), or you could increase the weights of instruction-following models in the merge.
I recommend steering the story using OOC comments, seem to be working really well for this model. Alternatively, you can create a Narrator Persona and switch to them to respond. Hope this helps!
I recommend steering the story using OOC comments, seem to be working really well for this model. Alternatively, you can create a Narrator Persona and switch to them to respond. Hope this helps!
@MarinaraSpaghetti
Hi, How do you steer the story using OOC comments? Could you give me a simple example?
At the end of my last message, I simply add:
(OOC: Hey, could you please do X from now on/steer the story this way? Thank you!)
Worked when I switched present tense to past tense out of nowhere, to do some testing. And it works if you want the model to include something specific.