Instructions to use dphn/dolphin-2.6-mixtral-8x7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphin-2.6-mixtral-8x7b with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphin-2.6-mixtral-8x7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.6-mixtral-8x7b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.6-mixtral-8x7b", 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-2.6-mixtral-8x7b 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-mixtral-8x7b"
# 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-mixtral-8x7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphin-2.6-mixtral-8x7b

SGLang

How to use dphn/dolphin-2.6-mixtral-8x7b 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-mixtral-8x7b" \
    --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-mixtral-8x7b",
		"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-mixtral-8x7b" \
        --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-mixtral-8x7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphin-2.6-mixtral-8x7b with Docker Model Runner:
```
docker model run hf.co/dphn/dolphin-2.6-mixtral-8x7b
```

Weird fine-tuning problem

by joorei - opened Dec 22, 2023

Discussion

joorei

Dec 22, 2023

Hello,

I am fine-tuning dolphin-mixtral with axolotl. I am inspired by your config, I chose qlora modules. What is interesting, that I can fine-tune dolphin-2.5-mixtral-8x7b, but when I just change "5" to "6" and otherwise keep the config the same (and remove and recreate the output directory), I get the following error:

 File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 1854, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 2735, in training_step
    loss = self.compute_loss(model, inputs)
  File "/workspace/axolotl/src/axolotl/core/trainer_builder.py", line 291, in compute_loss
    return super().compute_loss(model, inputs, return_outputs=return_outputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 2758, in compute_loss
    outputs = model(**inputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/utils/operations.py", line 659, in forward
    return model_forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/utils/operations.py", line 647, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/peft/peft_model.py", line 977, in forward
    return self.base_model(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/peft/tuners/tuners_utils.py", line 106, in forward
    return self.model.forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 164, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/models/mixtral/modeling_mixtral.py", line 1258, in forward
    loss += self.router_aux_loss_coef * aux_loss
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:6 and cuda:0!

Any idea what could be different about 2.5 vs 2.6 that could cause this?

gqd

Dec 27, 2023

https://github.com/OpenAccess-AI-Collective/axolotl/issues/998#issuecomment-1869140534 might be related

joorei

Jan 4, 2024

2.7 is the same btw.

I'm trying, but changing output_router_logits to false does not help

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment