Text Generation
Transformers
Safetensors
gemma2
mergekit
Merge
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Intelligence-Book")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Intelligence-Book")
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 SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: lodrick-the-lafted/ataraxy-actually-9b
- model: sam-paech/Quill-v1
merge_method: slerp
tokenizer_merge_method: slerp
tokenizer_parameters:
t: 0.3 # Dà più peso al tokenizer
base_model: sam-paech/Quill-v1
parameters:
t: [0, 0.2, 0.4, 0.5, 0.4, 0.2, 0] # Curva che favorisce leggermente
temp: 1.3 # Temperatura per smoothare il merge
density: # Density merging per bilanciare le caratteristiche dei due modelli
- threshold: 0.1
t: 0.7
- threshold: 0.5
t: 0.5
- threshold: 0.9
t: 0.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ClaudioItaly/Intelligence-Book") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)