Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use Nohobby/YetAnotherMerge-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nohobby/YetAnotherMerge-v0.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nohobby/YetAnotherMerge-v0.3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nohobby/YetAnotherMerge-v0.3") model = AutoModelForCausalLM.from_pretrained("Nohobby/YetAnotherMerge-v0.3") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Nohobby/YetAnotherMerge-v0.3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nohobby/YetAnotherMerge-v0.3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nohobby/YetAnotherMerge-v0.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nohobby/YetAnotherMerge-v0.3
- SGLang
How to use Nohobby/YetAnotherMerge-v0.3 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 "Nohobby/YetAnotherMerge-v0.3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nohobby/YetAnotherMerge-v0.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Nohobby/YetAnotherMerge-v0.3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nohobby/YetAnotherMerge-v0.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Nohobby/YetAnotherMerge-v0.3 with Docker Model Runner:
docker model run hf.co/Nohobby/YetAnotherMerge-v0.3
merge
This one turned out really good imo. Works fine with the basic min-p settings, but I recommend using this preset I made.
Merge Details
Merge Method
This model was merged using the della merge method using Undi95/Lumimaid-Magnum-12B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: Undi95/Lumimaid-Magnum-12B
parameters:
int8_mask: true
rescale: true
normalize: false
merge_method: della
dtype: bfloat16
models:
- model: Sao10K/MN-12B-Lyra-v1
parameters:
density: [0.4, 0.5, 0.6, 0.4, 0.6, 0.5, 0.4]
epsilon: [0.15, 0.15, 0.25, 0.15, 0.15]
lambda: 0.85
weight: [0.6, 0.5, 0.4, 0.6, 0.4, 0.5, 0.6]
- model: Nohobby/YetAnotherMerge-v0.1
parameters:
density: [0.45, 0.55, 0.45, 0.55, 0.45]
epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
lambda: 0.85
weight: [0.55, 0.45, 0.55, 0.45, 0.55]
- Downloads last month
- 3
Model tree for Nohobby/YetAnotherMerge-v0.3
Merge model
this model