Text Generation
Transformers
Safetensors
phi3
mergekit
Merge
conversational
custom_code
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Marsouuu/breadcrumbs3B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Marsouuu/breadcrumbs3B", trust_remote_code=True)
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
output-model
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the breadcrumbs merge method using microsoft/Phi-3-mini-4k-instruct as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: microsoft/Phi-3.5-mini-instruct
layer_range: [0, 32]
- model: microsoft/Phi-3-mini-4k-instruct
layer_range: [0, 32] # Limité aux 36 premières couches
merge_method: breadcrumbs
base_model: microsoft/Phi-3-mini-4k-instruct
parameters:
weight: 0.5 # Remplacer 'relative' par une valeur numérique
normalize: true
density: 0.9
gamma: 0.01
#t:
# - filter: self_attn
# value: [0.1, 0.25, 0.5, 0.75, 1] # Ajustement des poids pour self_attn
# - filter: mlp
# value: [1, 0.75, 0.5, 0.25, 0.1] # Ajustement des poids pour mlp
# - value: 0.5 # Valeur par défaut pour les autres couches
dtype: bfloat16
tokenizer_source: base
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Marsouuu/breadcrumbs3B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)