Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use Edens-Gate/DELLA-TEST-MERGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edens-Gate/DELLA-TEST-MERGE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Edens-Gate/DELLA-TEST-MERGE") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/DELLA-TEST-MERGE") model = AutoModelForCausalLM.from_pretrained("Edens-Gate/DELLA-TEST-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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Edens-Gate/DELLA-TEST-MERGE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Edens-Gate/DELLA-TEST-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": "Edens-Gate/DELLA-TEST-MERGE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Edens-Gate/DELLA-TEST-MERGE
- SGLang
How to use Edens-Gate/DELLA-TEST-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 "Edens-Gate/DELLA-TEST-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": "Edens-Gate/DELLA-TEST-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 "Edens-Gate/DELLA-TEST-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": "Edens-Gate/DELLA-TEST-MERGE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Edens-Gate/DELLA-TEST-MERGE with Docker Model Runner:
docker model run hf.co/Edens-Gate/DELLA-TEST-MERGE
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della_linear merge method using NewEden-Forge/madness-erebus as a base.
Models Merged
The following models were included in the merge:
- nbeerbower/mistral-nemo-bophades-12B
- TheDrummer/UnslopNemo-12B-v4.1
- Nitral-AI/Captain_BMO-12B
- nbeerbower/mistral-nemo-gutenberg-12B-v4
- NewEden-Forge/TEST-12B
Configuration
The following YAML configuration was used to produce this model:
models:
- model: Nitral-AI/Captain_BMO-12B
parameters:
weight: 0.1
density: 0.4
- model: nbeerbower/mistral-nemo-bophades-12B
parameters:
weight: 0.12
density: 0.5
- model: nbeerbower/mistral-nemo-gutenberg-12B-v4
parameters:
weight: 0.2
density: 0.6
- model: TheDrummer/UnslopNemo-12B-v4.1
parameters:
weight: 0.25
density: 0.7
- model: NewEden-Forge/TEST-12B
parameters:
weight: 0.33
density: 0.8
merge_method: della_linear
base_model: NewEden-Forge/madness-erebus
parameters:
epsilon: 0.05
lambda: 1
dtype: bfloat16
tokenizer_source: base
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