Instructions to use charioteer/Neural-phi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use charioteer/Neural-phi2 with Transformers:

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

pipe = pipeline("text-generation", model="charioteer/Neural-phi2", 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("charioteer/Neural-phi2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("charioteer/Neural-phi2", 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 charioteer/Neural-phi2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "charioteer/Neural-phi2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "charioteer/Neural-phi2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/charioteer/Neural-phi2

SGLang

How to use charioteer/Neural-phi2 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 "charioteer/Neural-phi2" \
    --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": "charioteer/Neural-phi2",
		"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 "charioteer/Neural-phi2" \
        --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": "charioteer/Neural-phi2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use charioteer/Neural-phi2 with Docker Model Runner:
```
docker model run hf.co/charioteer/Neural-phi2
```

charioteer commited on Apr 8, 2024

Commit

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@@ -9,16 +9,16 @@ pipeline_tag: text-generation
 ---
 # Model Card: Neural-phi2
-![Poster Image](path/to/poster.png)
 ## Model Details
 - **Model Name**: Neural-phi2
 - **Model Type**: Large Language Model (LLM)
 - **Model Architecture**: A finetuned version of the Phi2 model from Microsoft, utilizing Direct Preference Optimization (DPO) on the `distilabel-intel-orca-dpo-pairs` dataset.
-- **Model Size**: Approximately 7B parameters
 - **Training Data**: The model was finetuned on the `distilabel-intel-orca-dpo-pairs` dataset, which consists of chat-like prompts and responses.
-- **Training Procedure**: The Phi2 model was finetuned using the DPO technique as described in the Jupyter notebook. The training process involved:
   - Loading and formatting the `distilabel-intel-orca-dpo-pairs` dataset
   - Defining the training configuration, including batch size, learning rate, and number of epochs
   - Initializing the DPO Trainer and training the model

 ---
 # Model Card: Neural-phi2
+![Poster Image](poster.png)
 ## Model Details
 - **Model Name**: Neural-phi2
 - **Model Type**: Large Language Model (LLM)
 - **Model Architecture**: A finetuned version of the Phi2 model from Microsoft, utilizing Direct Preference Optimization (DPO) on the `distilabel-intel-orca-dpo-pairs` dataset.
+- **Model Size**: Approximately 2B parameters
 - **Training Data**: The model was finetuned on the `distilabel-intel-orca-dpo-pairs` dataset, which consists of chat-like prompts and responses.
+- **Training Procedure**: The Phi2 model was finetuned using the DPO technique. The training process involved:
   - Loading and formatting the `distilabel-intel-orca-dpo-pairs` dataset
   - Defining the training configuration, including batch size, learning rate, and number of epochs
   - Initializing the DPO Trainer and training the model