Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF

This model was converted to GGUF format from EpistemeAI/DeepPhi-3.5-mini-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model Summary

Reason Phi model for top performing model with it's size of 3.8B. Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length.

Run locally

4bit

After obtaining the Phi-3.5-mini-instruct model checkpoint, users can use this sample code for inference.

import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig

torch.random.manual_seed(0)

model_path = "EpistemeAI/DeepPhi-3.5-mini-instruct"

Configure 4-bit quantization using bitsandbytes

quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", # You can also try "fp4" if desired. bnb_4bit_compute_dtype=torch.float16 # Or torch.bfloat16 depending on your hardware. )

model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_path)

messages = [ {"role": "system", "content": """ You are a helpful AI assistant. Respond in the following format: ... ... """}, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving a 2x + 3 = 7 equation?"}, ]

def format_messages(messages): prompt = "" for msg in messages: role = msg["role"].capitalize() prompt += f"{role}: {msg['content']}\n" return prompt.strip()

prompt = format_messages(messages)

pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, )

generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, }

output = pipe(prompt, **generation_args) print(output[0]['generated_text'])

Uploaded model -

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

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

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -c 2048