How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Model-SafeTensors/MN-12B-Starcannon-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Model-SafeTensors/MN-12B-Starcannon-v3")
model = AutoModelForCausalLM.from_pretrained("Model-SafeTensors/MN-12B-Starcannon-v3", device_map="auto")
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]:]))
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Mistral Nemo 12B Starcannon v3

This is a merge of pre-trained language models created using mergekit.
Static GGUF (by Mradermacher)
Imatrix GGUF (by Mradermacher)
EXL2 (by kingbri of RoyalLab)

Merge Details

Merge Method

This model was merged using the TIES merge method using nothingiisreal/MN-12B-Celeste-V1.9 as a base.

Merge Fodder

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
    - model: anthracite-org/magnum-12b-v2
      parameters:
        density: 0.3
        weight: 0.5
    - model: nothingiisreal/MN-12B-Celeste-V1.9
      parameters:
        density: 0.7
        weight: 0.5

merge_method: ties
base_model: nothingiisreal/MN-12B-Celeste-V1.9
parameters:
    normalize: true
    int8_mask: true
dtype: bfloat16
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