Instructions to use g-ronimo/phi-2-OpenHermes-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use g-ronimo/phi-2-OpenHermes-2.5 with Transformers:

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

pipe = pipeline("text-generation", model="g-ronimo/phi-2-OpenHermes-2.5", 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("g-ronimo/phi-2-OpenHermes-2.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("g-ronimo/phi-2-OpenHermes-2.5", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use g-ronimo/phi-2-OpenHermes-2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "g-ronimo/phi-2-OpenHermes-2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "g-ronimo/phi-2-OpenHermes-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/g-ronimo/phi-2-OpenHermes-2.5

SGLang

How to use g-ronimo/phi-2-OpenHermes-2.5 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 "g-ronimo/phi-2-OpenHermes-2.5" \
    --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": "g-ronimo/phi-2-OpenHermes-2.5",
		"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 "g-ronimo/phi-2-OpenHermes-2.5" \
        --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": "g-ronimo/phi-2-OpenHermes-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use g-ronimo/phi-2-OpenHermes-2.5 with Docker Model Runner:
```
docker model run hf.co/g-ronimo/phi-2-OpenHermes-2.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

microsoft/phi-2 + teknium/OpenHermes-2.5

Training

QLoRA rank 32, LR 2e-5, 1 epoch
effective batch size: 200
max. seq. length: 1024 tokens
code in code/

Evals

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
g-ronimo/phi-2-OpenHermes-2.5	30.27	71.18	43.87	35.9	45.3
minghaowu/phi-2-OpenHermes-2.5	27.95	67.55	48.07	36.17	44.94
phi-2	27.96	70.84	44.46	35.17	44.61

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

modelpath="g-ronimo/phi-2-OpenHermes-2.5"

model = AutoModelForCausalLM.from_pretrained(
    modelpath,    
    torch_dtype=torch.bfloat16,
    device_map="auto",
    # attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(modelpath) 

messages = [
    {"role": "system", "content": "answer like a pirate"},
    {"role": "user", "content": "what does it mean to be successful?"},
]
        
input_tokens = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")
output_tokens = model.generate(input_tokens, max_new_tokens=500)
output = tokenizer.decode(output_tokens[0])

print(output)

Ahoy there, matey! To me, being successful means having the wind in your sails and reaching the treasure you've been dreaming of. It's about setting sail on a journey with clear goals, working hard, facing challenges head-on, and never losing sight of what truly matters. So, set your compass right, hoist your Jolly Roger high, and let's embark on this adventure together! ⚓️💰⛵️

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for g-ronimo/phi-2-OpenHermes-2.5

Quantizations

2 models

g-ronimo
/

phi-2-OpenHermes-2.5

microsoft/phi-2 + teknium/OpenHermes-2.5

Training

Evals

Inference

Model tree for g-ronimo/phi-2-OpenHermes-2.5

Dataset used to train g-ronimo/phi-2-OpenHermes-2.5