Instructions to use Undi95/Mistral-7B-roleplay_alpaca-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/Mistral-7B-roleplay_alpaca-lora with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/Mistral-7B-roleplay_alpaca-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/Mistral-7B-roleplay_alpaca-lora")
model = AutoModelForCausalLM.from_pretrained("Undi95/Mistral-7B-roleplay_alpaca-lora")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Undi95/Mistral-7B-roleplay_alpaca-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/Mistral-7B-roleplay_alpaca-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Mistral-7B-roleplay_alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/Mistral-7B-roleplay_alpaca-lora

SGLang

How to use Undi95/Mistral-7B-roleplay_alpaca-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 "Undi95/Mistral-7B-roleplay_alpaca-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": "Undi95/Mistral-7B-roleplay_alpaca-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 "Undi95/Mistral-7B-roleplay_alpaca-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": "Undi95/Mistral-7B-roleplay_alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/Mistral-7B-roleplay_alpaca-lora with Docker Model Runner:
```
docker model run hf.co/Undi95/Mistral-7B-roleplay_alpaca-lora
```

roleplay_alpaca

This model was trained from scratch on the TokenBender/roleplay_alpaca dataset. It achieves the following results on the evaluation set:

Loss: 1.2973

Model description

Dataset trained on mistralai/Mistral-7B-v0.1

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00065
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.5404	0.03	20	1.2764
1.4523	0.06	40	1.2571
1.5608	0.09	60	1.2465
1.5921	0.12	80	1.2575
1.6043	0.15	100	1.2432
1.5496	0.18	120	1.2504
1.348	0.21	140	1.2452
1.4638	0.24	160	1.2661
1.5733	0.27	180	1.2548
1.5397	0.3	200	1.2674
1.6154	0.33	220	1.2626
1.5058	0.36	240	1.2672
1.3974	0.4	260	1.2659
1.6654	0.43	280	1.2648
1.8051	0.46	300	1.2585
1.7487	0.49	320	1.2736
1.3612	0.52	340	1.2717
1.5048	0.55	360	1.2809
1.7134	0.58	380	1.2885
1.5524	0.61	400	1.2805
1.3705	0.64	420	1.2860
1.4335	0.67	440	1.2896
1.3642	0.7	460	1.2911
1.6546	0.73	480	1.2888
1.5345	0.76	500	1.2973
1.5968	0.79	520	1.2885
1.5694	0.82	540	1.2939
1.5474	0.85	560	1.2892
1.6981	0.88	580	1.2949
1.5451	0.91	600	1.2886
1.5845	0.94	620	1.2941
1.5143	0.97	640	1.2973

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.0

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