Instructions to use DataCanvas/Alaya-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataCanvas/Alaya-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="DataCanvas/Alaya-7B-Chat", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataCanvas/Alaya-7B-Chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DataCanvas/Alaya-7B-Chat", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DataCanvas/Alaya-7B-Chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DataCanvas/Alaya-7B-Chat

SGLang

How to use DataCanvas/Alaya-7B-Chat 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 "DataCanvas/Alaya-7B-Chat" \
    --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": "DataCanvas/Alaya-7B-Chat",
		"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 "DataCanvas/Alaya-7B-Chat" \
        --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": "DataCanvas/Alaya-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DataCanvas/Alaya-7B-Chat with Docker Model Runner:
```
docker model run hf.co/DataCanvas/Alaya-7B-Chat
```

Update modeling_mpt.py

by fyfyfyfy28 - opened Dec 12, 2023

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Files changed (1) hide show

modeling_mpt.py +1 -1

modeling_mpt.py CHANGED Viewed

@@ -20,7 +20,7 @@ from .ffn import build_ffn as build_ffn
 from .norm import NORM_CLASS_REGISTRY
 from .configuration_mpt import MPTConfig
 from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
-from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
 from .meta_init_context import init_empty_weights
 from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY
 try:

 from .norm import NORM_CLASS_REGISTRY
 from .configuration_mpt import MPTConfig
 from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
+# from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
 from .meta_init_context import init_empty_weights
 from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY
 try: