Instructions to use maicomputer/alpaca-native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maicomputer/alpaca-native with Transformers:

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

pipe = pipeline("text-generation", model="maicomputer/alpaca-native")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maicomputer/alpaca-native")
model = AutoModelForCausalLM.from_pretrained("maicomputer/alpaca-native")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maicomputer/alpaca-native with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/maicomputer/alpaca-native

SGLang

How to use maicomputer/alpaca-native 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 "maicomputer/alpaca-native" \
    --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": "maicomputer/alpaca-native",
		"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 "maicomputer/alpaca-native" \
        --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": "maicomputer/alpaca-native",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maicomputer/alpaca-native with Docker Model Runner:
```
docker model run hf.co/maicomputer/alpaca-native
```

chavinlo commited on Mar 20, 2023

Commit

d689ebe

1 Parent(s): 30b0f97

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -6

README.md CHANGED Viewed

@@ -1,12 +1,18 @@
-Stanford Alpaca
-FINETUNED USING THE ORIGINAL REPOSITORY: https://github.com/tatsu-lab/stanford_alpaca
-NO LORA HAS BEEN USED
-full model 3 epochs og data
-CONFIGURATION (default):
 ```shell
 torchrun --nproc_per_node=4 --master_port=3045 train.py \
@@ -27,7 +33,7 @@ torchrun --nproc_per_node=4 --master_port=3045 train.py \
     --warmup_ratio 0.03 \
     --lr_scheduler_type "cosine" \
     --logging_steps 1 \
-    --fsdp "full_shard auto_wrap" \
     --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \
     --tf32 True --report_to="wandb"
 ```

+# Stanford Alpaca
+This is a replica of Alpaca by Stanford' tatsu
+Trained using the original instructions with a minor modification in FSDP mode
+Trained on 4xA100s for 6H
+NO LORA HAS BEEN USED, this is a natively-finetuned model, hence "alpaca-native"
+If you are interested on more llama-based models, you can check out my profile or search for other models at https://huggingface.co/models?other=llama
+This (MIGHT) be a quantized version of this model, but be careful: https://boards.4channel.org/g/thread/92173062#p92182396
+CONFIGURATION (default except fsdp):
 ```shell
 torchrun --nproc_per_node=4 --master_port=3045 train.py \
     --warmup_ratio 0.03 \
     --lr_scheduler_type "cosine" \
     --logging_steps 1 \
+    --fsdp "shard_grad_op auto_wrap" \
     --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \
     --tf32 True --report_to="wandb"
 ```