Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use Edens-Gate/Chunky-Merge-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edens-Gate/Chunky-Merge-V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Edens-Gate/Chunky-Merge-V3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Chunky-Merge-V3") model = AutoModelForCausalLM.from_pretrained("Edens-Gate/Chunky-Merge-V3") 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/Chunky-Merge-V3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Edens-Gate/Chunky-Merge-V3" # 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/Chunky-Merge-V3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Edens-Gate/Chunky-Merge-V3
- SGLang
How to use Edens-Gate/Chunky-Merge-V3 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/Chunky-Merge-V3" \ --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/Chunky-Merge-V3", "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/Chunky-Merge-V3" \ --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/Chunky-Merge-V3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Edens-Gate/Chunky-Merge-V3 with Docker Model Runner:
docker model run hf.co/Edens-Gate/Chunky-Merge-V3
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Chunky-Merge-V3")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/Chunky-Merge-V3")
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]:]))Quick Links
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 grimjim/mistralai-Mistral-Nemo-Instruct-2407 as a base.
Models Merged
The following models were included in the merge:
- intervitens/mini-magnum-12b-v1.1
- Gryphe/Pantheon-RP-1.6.1-12b-Nemo
- anthracite-org/magnum-v4-12b
- anthracite-org/magnum-v2.5-12b-kto
- TheDrummer/Rocinante-12B-v1.1
Configuration
The following YAML configuration was used to produce this model:
models:
- model: Gryphe/Pantheon-RP-1.6.1-12b-Nemo
parameters:
weight: 0.35
density: 0.9
- model: TheDrummer/Rocinante-12B-v1.1
parameters:
weight: 0.25
density: 0.8
- model: anthracite-org/magnum-v4-12b
parameters:
weight: 0.15
density: 0.7
- model: intervitens/mini-magnum-12b-v1.1
parameters:
weight: 0.15
density: 0.6
- model: anthracite-org/magnum-v2.5-12b-kto
parameters:
weight: 0.10
density: 0.5
merge_method: della_linear
base_model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
parameters:
epsilon: 0.01
lambda: 0.8
dtype: bfloat16
tokenizer_source: base
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Edens-Gate/Chunky-Merge-V3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)