Instructions to use dphn/dolphin-2_6-phi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphin-2_6-phi-2 with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphin-2_6-phi-2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2_6-phi-2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dphn/dolphin-2_6-phi-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/dolphin-2_6-phi-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dphn/dolphin-2_6-phi-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphin-2_6-phi-2

SGLang

How to use dphn/dolphin-2_6-phi-2 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 "dphn/dolphin-2_6-phi-2" \
    --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": "dphn/dolphin-2_6-phi-2",
		"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 "dphn/dolphin-2_6-phi-2" \
        --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": "dphn/dolphin-2_6-phi-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphin-2_6-phi-2 with Docker Model Runner:
```
docker model run hf.co/dphn/dolphin-2_6-phi-2
```

Update README.md

#13

by DavidGF - opened Mar 4, 2024

base: refs/heads/main

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@@ -36,6 +36,22 @@ New in 2.6
 This model is uncensored.  I have filtered the dataset to remove alignment and bias.  This makes the model more compliant.  You are advised to implement your own alignment layer before exposing the model as a service.  It will be highly compliant to any requests, even unethical ones.  Please read my blog post about uncensored models.  https://erichartford.com/uncensored-models
 You are responsible for any content you create using this model.  Enjoy responsibly.
 ## Training
 It took 2 days to train 3 epochs on 4x A100s using qLoRA and Axolotl

 This model is uncensored.  I have filtered the dataset to remove alignment and bias.  This makes the model more compliant.  You are advised to implement your own alignment layer before exposing the model as a service.  It will be highly compliant to any requests, even unethical ones.  Please read my blog post about uncensored models.  https://erichartford.com/uncensored-models
 You are responsible for any content you create using this model.  Enjoy responsibly.
+## Evaluation
+**Open LLM Leaderboard:**
+benchmarked on lm-evaluation-harness v.0.4.1
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | **61.7**  |
+| ARC (25-shot)         | 59.81         |
+| HellaSwag (10-shot)   | 74.65  |
+| MMLU (5-shot)         | 55.38|
+| TruthfulQA (0-shot)   | 47.39 |
+| Winogrande (5-shot)   | 74.90  |
+| GSM8K (5-shot)        | 58.07        |
 ## Training
 It took 2 days to train 3 epochs on 4x A100s using qLoRA and Axolotl