Instructions to use dphn/dolphin-2.9.2-Phi-3-Medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphin-2.9.2-Phi-3-Medium with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphin-2.9.2-Phi-3-Medium")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9.2-Phi-3-Medium")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9.2-Phi-3-Medium", 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 dphn/dolphin-2.9.2-Phi-3-Medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/dolphin-2.9.2-Phi-3-Medium"
# 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-2.9.2-Phi-3-Medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphin-2.9.2-Phi-3-Medium

SGLang

How to use dphn/dolphin-2.9.2-Phi-3-Medium 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-2.9.2-Phi-3-Medium" \
    --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-2.9.2-Phi-3-Medium",
		"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-2.9.2-Phi-3-Medium" \
        --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-2.9.2-Phi-3-Medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphin-2.9.2-Phi-3-Medium with Docker Model Runner:
```
docker model run hf.co/dphn/dolphin-2.9.2-Phi-3-Medium
```

dolphin-2.9.2-Phi-3-Medium / README.md

ehartford

Update README.md

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	---
	license: mit
	language:
	- en
	base_model:
	- unsloth/Phi-3-mini-4k-instruct
	datasets:
	- cognitivecomputations/Dolphin-2.9.2
	- teknium/OpenHermes-2.5
	- m-a-p/CodeFeedback-Filtered-Instruction
	- cognitivecomputations/dolphin-coder
	- cognitivecomputations/samantha-data
	- microsoft/orca-math-word-problems-200k
	- internlm/Agent-FLAN
	- cognitivecomputations/SystemChat-2.0
	---

	# Dolphin 2.9.2 Phi 3 Medium 🐬

	Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations

	[![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations)
	Discord: https://discord.gg/cognitivecomputations

	<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />

	Our appreciation for the sponsor of Dolphin 2.9.2:
	- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40Snode

	This model is based on Phi-3-Medium-Instruct-4k, and is governed by the MIT license with which Microsoft released Phi-3.

	Since Microsoft only released the fine-tuned model - Dolphin-2.9.2-Phi-3-Medium has not been entirely cleaned of refusals.

	The base model has 4k context, and the qLoRA fine-tuning was with 4k sequence length.

	It took 3.5 days on 8xL40S node provided by Crusoe Cloud

	This model uses the ChatML prompt template.

	example:

	```
	<\|im_start\|>system
	You are Dolphin, a helpful AI assistant.<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant

	```

	Dolphin-2.9.2 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
	We 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. 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.


	![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/3dLaIlx3pVme2jWEtwbNp.png)

	[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

	## evals:
	<img src="https://i.ibb.co/jrBsPLY/file-9gw-A1-Ih-SBYU3-PCZ92-ZNb-Vci-P.png" width="600" />