Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Chunky-Merge-V2")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/Chunky-Merge-V2")
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 intervitens/mini-magnum-12b-v1.1 as a base.
Models Merged
The following models were included in the merge:
- Gryphe/Pantheon-RP-1.6.1-12b-Nemo
- anthracite-org/magnum-v2.5-12b-kto
- anthracite-org/magnum-v4-12b
- grimjim/mistralai-Mistral-Nemo-Instruct-2407
- TheDrummer/Rocinante-12B-v1.1
Configuration
The following YAML configuration was used to produce this model:
models:
- model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
parameters:
weight: 0.15
density: 0.5
- model: Gryphe/Pantheon-RP-1.6.1-12b-Nemo
parameters:
weight: 0.18
density: 0.6
- model: TheDrummer/Rocinante-12B-v1.1
parameters:
weight: 0.22
density: 0.7
- model: anthracite-org/magnum-v4-12b
parameters:
weight: 0.25
density: 0.8
- model: anthracite-org/magnum-v2.5-12b-kto
parameters:
weight: 0.20
density: 0.75
merge_method: della_linear
base_model: intervitens/mini-magnum-12b-v1.1
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-V2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)