Instructions to use BucketOfFish/simplified_phi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BucketOfFish/simplified_phi2 with Transformers:

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

pipe = pipeline("text-generation", model="BucketOfFish/simplified_phi2", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BucketOfFish/simplified_phi2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BucketOfFish/simplified_phi2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BucketOfFish/simplified_phi2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BucketOfFish/simplified_phi2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BucketOfFish/simplified_phi2

SGLang

How to use BucketOfFish/simplified_phi2 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 "BucketOfFish/simplified_phi2" \
    --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": "BucketOfFish/simplified_phi2",
		"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 "BucketOfFish/simplified_phi2" \
        --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": "BucketOfFish/simplified_phi2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BucketOfFish/simplified_phi2 with Docker Model Runner:
```
docker model run hf.co/BucketOfFish/simplified_phi2
```

BucketOfFish commited on Jan 6, 2024

Commit

4f25dda

1 Parent(s): c07c430

Fixed inference script bug and made deterministic

Browse files

Files changed (1) hide show

streaming_inference.py +2 -1

streaming_inference.py CHANGED Viewed

@@ -39,11 +39,12 @@ if __name__ == "__main__":
             key = key.replace("lm_head.linear.", "lm_head_linear.")
         model_state_dict[key] = value
     model.load_state_dict(model_state_dict)
     thread = Thread(
         target=model.generate,
         kwargs=dict(
-            inputs=tokenizer(  # returns a torch dictionary
                 "Here is an essay on sea monkeys: ",
                 return_tensors="pt",
                 return_attention_mask=False,

             key = key.replace("lm_head.linear.", "lm_head_linear.")
         model_state_dict[key] = value
     model.load_state_dict(model_state_dict)
+    model.eval()
     thread = Thread(
         target=model.generate,
         kwargs=dict(
+            tokenizer(  # returns a torch dictionary
                 "Here is an essay on sea monkeys: ",
                 return_tensors="pt",
                 return_attention_mask=False,