Instructions to use MrOvkill/Phi-3-Instruct-Bloated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrOvkill/Phi-3-Instruct-Bloated with Transformers:

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

pipe = pipeline("text-generation", model="MrOvkill/Phi-3-Instruct-Bloated", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MrOvkill/Phi-3-Instruct-Bloated", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MrOvkill/Phi-3-Instruct-Bloated", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MrOvkill/Phi-3-Instruct-Bloated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MrOvkill/Phi-3-Instruct-Bloated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrOvkill/Phi-3-Instruct-Bloated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MrOvkill/Phi-3-Instruct-Bloated

SGLang

How to use MrOvkill/Phi-3-Instruct-Bloated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MrOvkill/Phi-3-Instruct-Bloated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrOvkill/Phi-3-Instruct-Bloated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MrOvkill/Phi-3-Instruct-Bloated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrOvkill/Phi-3-Instruct-Bloated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MrOvkill/Phi-3-Instruct-Bloated with Docker Model Runner:
```
docker model run hf.co/MrOvkill/Phi-3-Instruct-Bloated
```

Phi-3-Instruct-Bloated

Phi-3-Instruct-Bloated is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: microsoft/Phi-3-mini-128k-instruct
        layer_range: [0, 32]
      - model: NexaAIDev/Octopus-v4
        layer_range: [0, 32]
merge_method: slerp
base_model: microsoft/Phi-3-mini-128k-instruct
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

# Installation
!pip install -qU transformers accelerate

# Imports
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Loading
tokenizer = AutoTokenizer.from_pretrained("MrOvkill/Phi-3-Instruct-Bloated")
model = AutoModelForCausalLM.from_pretrained("MrOvkill/Phi-3-Instruct-Bloated")

# Completion function
def infer(prompt, **kwargs):
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(**inputs, **kwargs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Some silliness
infer("<|user|>\nBen is going to the store for some Ice Cream. So is Jerry. They mix up the ice cream at the store. Is the ice cream: (a. Ben's (b. Jerry's (c. Ben and Jerry's <|end|>\n<|assistant|>\nMy answer is (", max_new_tokens=1024)

# A proper test
infer(
    """
<|user|>
Explain what a Mixture of Experts is in less than 100 words.
<|assistant|>
""",
    max_new_tokens=1024,
    do_sample=False,
    temperature=0.0,
    top_k=50,
    top_p=0.89,
)

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for MrOvkill/Phi-3-Instruct-Bloated

NexaAI/octo-net

microsoft/Phi-3-mini-128k-instruct

Merge model

this model