Text Generation
Transformers
Safetensors
English
phi3
Merge
mergekit
lazymergekit
microsoft/Phi-3-mini-128k-instruct
NexaAIDev/Octopus-v4
conversational
custom_code
text-generation-inference
Instructions to use MrOvkill/Phi-3-Instruct-Bloated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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, device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
File size: 1,454 Bytes
ccb82da 83a5aeb 89c1b74 83a5aeb deb69ba 83a5aeb ccb82da df4183f 83a5aeb ccb82da 3c57069 dc0013e 42565ce 3c57069 ccb82da deb69ba ccb82da f72c5f5 937acbc deb69ba 937acbc deb69ba 83a5aeb ccb82da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import json
import os
from typing import Dict, List, Any
import torch
from transformers import pipeline
PROMPT_FORMAT= """
<|user|>
{inputs} <|end|>
<|assistant|>
"""
class EndpointHandler():
def __init__(self, data):
cfg = {
"repo": "MrOvkill/Phi-3-Instruct-Bloated",
}
self.pipe = pipeline("text-generation", "MrOvkill/Phi-3-Instruct-Bloated", torch_dtype=torch.float16, trust_remote_code=True)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
self.pipe = pipeline("text-generation", "MrOvkill/Phi-3-Instruct-Bloated", torch_dtype=torch.float16, trust_remote_code=True)
max_new_tokens = 1024
if "max_new_tokens" in data:
max_new_tokens = data["max_new_tokens"]
try:
max_new_tokens = int(max_new_tokens)
except Exception as e:
return json.dumps({
"status": "error",
"reason": "max_length was passed as something that was absolutely not a plain old int"
})
res = PROMPT_FORMAT.format(inputs=data['inputs'])
return self.pipe(
res,
do_sample=False,
max_new_tokens=max_new_tokens
)
return res |