Instructions to use dphn/dolphin-2.9-llama3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphin-2.9-llama3-8b with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphin-2.9-llama3-8b")
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-llama3-8b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9-llama3-8b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dphn/dolphin-2.9-llama3-8b 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-llama3-8b"
# 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-llama3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphin-2.9-llama3-8b

SGLang

How to use dphn/dolphin-2.9-llama3-8b 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-llama3-8b" \
    --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-llama3-8b",
		"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-llama3-8b" \
        --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-llama3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphin-2.9-llama3-8b with Docker Model Runner:
```
docker model run hf.co/dphn/dolphin-2.9-llama3-8b
```

Poor quality output compared to Meta's Instruction Tune

by ElliottDyson - opened Apr 21, 2024

Discussion

ElliottDyson

Apr 21, 2024

Dear cognitivecomputations, I love the work you do here, but I (and other users I have seen post about this on reddit) have noticed a significant degradation in instruction following, and in many other aspects too, such as the ability for the model to generate very human-like text.

I was wondering if perhaps it might be possible that an earlier checkpoint in training might mitigate this whilst maintaining the benefits you are attempting to achieve, or perhaps it was the original instruction tuning that made the responses so good? If that is the case, then maybe it is worth fine-tuning with a converted dataset that uses the llama3 instruction prompt format instead, perhaps with the earlier checkpoint as well?

Thank you.

Mikael110

Apr 21, 2024

•

edited Apr 21, 2024

It's worth noting that this is a finetune of the base version of Llama-3, not the instruction tuned version, which it sounds like you are comparing it to. So the "original" in this case had no instruction following ability at all. Because of that it would also make little difference what chat template it is trained with, as the base version has no native chat template to start with.

ElliottDyson

Apr 21, 2024

•

edited Apr 21, 2024

Understood. That would explain the poorer performance on chat related tasks compared to the instruction tune. Would love to see this done on the instruct model!😁

Thank you.

ElliottDyson changed discussion title from Poor quality output compared to original to Poor quality output compared to Meta's Instruction Tune Apr 21, 2024

ElliottDyson

Apr 21, 2024

Due to realisation that this is not the purpose of this model from the following Reddit comment: https://www.reddit.com/r/LocalLLaMA/s/0FLP3MjLI0

I have decided to close this thread.

Thank you 😊

ElliottDyson changed discussion status to closed Apr 21, 2024

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