Instructions to use poisson-fish/Causal-Dolphin-Agent-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use poisson-fish/Causal-Dolphin-Agent-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="poisson-fish/Causal-Dolphin-Agent-LoRA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("poisson-fish/Causal-Dolphin-Agent-LoRA")
model = AutoModelForCausalLM.from_pretrained("poisson-fish/Causal-Dolphin-Agent-LoRA")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use poisson-fish/Causal-Dolphin-Agent-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "poisson-fish/Causal-Dolphin-Agent-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poisson-fish/Causal-Dolphin-Agent-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/poisson-fish/Causal-Dolphin-Agent-LoRA

SGLang

How to use poisson-fish/Causal-Dolphin-Agent-LoRA 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 "poisson-fish/Causal-Dolphin-Agent-LoRA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poisson-fish/Causal-Dolphin-Agent-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "poisson-fish/Causal-Dolphin-Agent-LoRA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poisson-fish/Causal-Dolphin-Agent-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use poisson-fish/Causal-Dolphin-Agent-LoRA with Docker Model Runner:
```
docker model run hf.co/poisson-fish/Causal-Dolphin-Agent-LoRA
```

Causal-Dolphin-Agent-v0.1

This model is a LoRA fine-tune of CausalLM/7B on Eric's wonderful Dolphin dataset, with THUDM/AgentInstruct mixed in both training runs.

Causal-Dolphin-Agent was trained for 3 epochs on 5 million GPT3.5 augmented FLAN instructions & AgentInstruct dataset in ChatML format. It was then trained a further 3 epochs on 1 million GPT4 augmented FLAN instructions with AgentInstruct shuffled in as well.

It achieves the following results on the evaluation set:

Loss: 2.8435

Prompt Format

Causal-Dolphin-Agent uses ChatML as the prompt format:

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
If Danny owns a bike, then Edward owns a bike. If Edward owns a bike, then Freddy owns a bike. If Danny owns a bike, which of the following statements must be true? Let's think step by step.

I. Edward owns a bike.
II. Freddy owns a bike.
III. Freddy does not own a bike.

Choose one answer:
I only
II only
III only
I and II only
I and III only
<|im_end|>
<|im_start|>assistant

Training and evaluation data

ehartford/dolphin THUDM/AgentInstruct

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.7774	0.0	1	5.1009
3.2798	0.15	46	5.1010
2.0722	0.3	92	5.0489
2.5919	0.45	138	4.8834
2.0011	0.6	184	4.6678
1.3733	0.75	230	4.4628
1.7321	0.9	276	4.2757
1.3994	1.05	322	4.1029
1.2308	1.2	368	3.8916
0.8229	1.35	414	3.6451
0.9592	1.5	460	3.4106
0.8528	1.65	506	3.2250
0.7362	1.8	552	3.0852
0.8077	1.95	598	2.9881
0.6912	2.1	644	2.9315
0.7776	2.25	690	2.8911
0.6916	2.41	736	2.8678
0.8674	2.56	782	2.8534
0.7797	2.71	828	2.8545
0.6838	2.86	874	2.8435

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.14.1

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