base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit language:

en license: apache-2.0 tags:
text-generation-inference
transformers
unsloth
phi
gguf datasets:
mlabonne/FineTome-100k
microsoft/orca-math-word-problems-200k
m-a-p/CodeFeedback-Filtered-Instruction
cognitivecomputations/dolphin-coder
PawanKrd/math-gpt-4o-200k
V3N0M/Jenna-50K-Alpaca-Uncensored

Phi-3.5-mini-instruct-uncensored

Developed by: Carsen Klock
License: apache-2.0
Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit

This Phi3.5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Trained on 1 x 4080 SUPER over 10500 Epochs as a test. This is for test purposes only.

GGUFs are included in this repository for inference

Running in transformers

# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="carsenk/phi3.5_mini_exp_825_uncensored")
pipe(messages)

print(pipe(messages))

Running in llama.cpp (Use GGUF)

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="carsenk/phi3.5_mini_exp_825_uncensored",
	filename="unsloth.BF16.gguf",
)

llm.create_chat_completion(
		messages = [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
)

Phi-3.5-mini-instruct-uncensored

Developed by: Carsen Klock

License: apache-2.0

Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit

This Phi3.5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Trained on 1 x 4080 SUPER over 10500 Epochs as a test. This is for test purposes only.

GGUFs are included in this repository for inference

Running in transformers

# Use a pipeline as a high-level helper from transformers import pipeline messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="carsenk/phi3.5_mini_exp_825_uncensored") pipe(messages) print(pipe(messages))

Running in llama.cpp (Use GGUF)

from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="carsenk/phi3.5_mini_exp_825_uncensored", filename="unsloth.BF16.gguf", ) llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] )