Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use Slomb/MN-CelesteGold-12B-Merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Slomb/MN-CelesteGold-12B-Merge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Slomb/MN-CelesteGold-12B-Merge") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Slomb/MN-CelesteGold-12B-Merge") model = AutoModelForCausalLM.from_pretrained("Slomb/MN-CelesteGold-12B-Merge") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Slomb/MN-CelesteGold-12B-Merge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Slomb/MN-CelesteGold-12B-Merge" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Slomb/MN-CelesteGold-12B-Merge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Slomb/MN-CelesteGold-12B-Merge
- SGLang
How to use Slomb/MN-CelesteGold-12B-Merge 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 "Slomb/MN-CelesteGold-12B-Merge" \ --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": "Slomb/MN-CelesteGold-12B-Merge", "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 "Slomb/MN-CelesteGold-12B-Merge" \ --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": "Slomb/MN-CelesteGold-12B-Merge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Slomb/MN-CelesteGold-12B-Merge with Docker Model Runner:
docker model run hf.co/Slomb/MN-CelesteGold-12B-Merge
merge
This is a merge of pre-trained language models created using mergekit.
A combination of the two most promising models for roleplay chatbots as of 08/26/2024. I am not affiliated with any of the models used in the merge.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: elinas/Chronos-Gold-12B-1.0
layer_range:
- 0
- 40
- model: nothingiisreal/MN-12B-Celeste-V1.9
layer_range:
- 0
- 40
merge_method: slerp
base_model: elinas/Chronos-Gold-12B-1.0
parameters:
t:
- filter: self_attn
value:
- 0
- 0.5
- 0.3
- 0.7
- 1
- filter: mlp
value:
- 1
- 0.5
- 0.7
- 0.3
- 0
- value: 0.5
dtype: bfloat16
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