Instructions to use SteelStorage/phi-2-DLEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SteelStorage/phi-2-DLEC with Transformers:

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

pipe = pipeline("text-generation", model="SteelStorage/phi-2-DLEC", 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("SteelStorage/phi-2-DLEC", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SteelStorage/phi-2-DLEC", 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 SteelStorage/phi-2-DLEC with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SteelStorage/phi-2-DLEC

SGLang

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

Docker Model Runner
How to use SteelStorage/phi-2-DLEC with Docker Model Runner:
```
docker model run hf.co/SteelStorage/phi-2-DLEC
```

Steelskull commited on Mar 25, 2024

Commit

cd310f0

verified ·

1 Parent(s): 9467ed2

Update README.md

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@@ -64,13 +64,13 @@ AGIEval 31.95
 GPT4All 70.81
 TruthfulQA 48.39
 ```
 Loss or Increase:
 Avg. -0.44
 AGIEval -2.31
 GPT4All -1.33
 TruthfulQA +1.90
 Example of loss:
 [Steelskull/Etheria-55b-v0.1](https://huggingface.co/Steelskull/Etheria-55b-v0.1)
@@ -96,7 +96,7 @@ TruthfulQA 56.31
 Winogrande 82.79
 GSM8k 65.43
 ```
 Merge Loss (Yi-34B-200K-DARE-megamerge-v8 compared to Etheria-55b-v0.1):
 Avg. -7.87
 AI2 Reasoning Challenge -2.65
@@ -105,7 +105,7 @@ MMLU -3.37
 TruthfulQA +0.15
 Winogrande -6.70
 GSM8k -30.25
 In the example comparing Etheria-55b-v0.1 and Yi-34B-200K-DARE-megamerge-v8, there is a significant decrease in performance across all metrics, with the average score decreasing by 7.87 points. The most notable is in the GSM8k benchmark, where Yi-34B-200K-DARE-megamerge-v8 outperforms Etheria-55b-v0.1 by 30.25 points.
 This method is still in active development, and I am currently tweaking the algorithm to improve the layer selection process,

 GPT4All 70.81
 TruthfulQA 48.39
 ```
+```
 Loss or Increase:
 Avg. -0.44
 AGIEval -2.31
 GPT4All -1.33
 TruthfulQA +1.90
+```
 Example of loss:
 [Steelskull/Etheria-55b-v0.1](https://huggingface.co/Steelskull/Etheria-55b-v0.1)
 Winogrande 82.79
 GSM8k 65.43
 ```
+```
 Merge Loss (Yi-34B-200K-DARE-megamerge-v8 compared to Etheria-55b-v0.1):
 Avg. -7.87
 AI2 Reasoning Challenge -2.65
 TruthfulQA +0.15
 Winogrande -6.70
 GSM8k -30.25
+```
 In the example comparing Etheria-55b-v0.1 and Yi-34B-200K-DARE-megamerge-v8, there is a significant decrease in performance across all metrics, with the average score decreasing by 7.87 points. The most notable is in the GSM8k benchmark, where Yi-34B-200K-DARE-megamerge-v8 outperforms Etheria-55b-v0.1 by 30.25 points.
 This method is still in active development, and I am currently tweaking the algorithm to improve the layer selection process,