Instructions to use teddy-f-47/phi-pl-2_7B-v_0_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teddy-f-47/phi-pl-2_7B-v_0_1 with Transformers:

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

pipe = pipeline("text-generation", model="teddy-f-47/phi-pl-2_7B-v_0_1", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teddy-f-47/phi-pl-2_7B-v_0_1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("teddy-f-47/phi-pl-2_7B-v_0_1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use teddy-f-47/phi-pl-2_7B-v_0_1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teddy-f-47/phi-pl-2_7B-v_0_1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teddy-f-47/phi-pl-2_7B-v_0_1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teddy-f-47/phi-pl-2_7B-v_0_1

SGLang

How to use teddy-f-47/phi-pl-2_7B-v_0_1 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 "teddy-f-47/phi-pl-2_7B-v_0_1" \
    --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": "teddy-f-47/phi-pl-2_7B-v_0_1",
		"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 "teddy-f-47/phi-pl-2_7B-v_0_1" \
        --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": "teddy-f-47/phi-pl-2_7B-v_0_1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teddy-f-47/phi-pl-2_7B-v_0_1 with Docker Model Runner:
```
docker model run hf.co/teddy-f-47/phi-pl-2_7B-v_0_1
```

teddy-f-47 commited on Mar 1, 2024

Commit

eeb401c

verified ·

1 Parent(s): d0f5976

Update configuration_phi.py

Browse files

Files changed (1) hide show

configuration_phi.py +9 -9

configuration_phi.py CHANGED Viewed

@@ -116,13 +116,13 @@ class PhiConfig(PretrainedConfig):
     def __init__(
         self,
-        vocab_size=51200,
-        hidden_size=2048,
-        intermediate_size=8192,
-        num_hidden_layers=24,
         num_attention_heads=32,
-        num_key_value_heads=None,
-        resid_pdrop=0.0,
         embd_pdrop=0.0,
         attention_dropout=0.0,
         hidden_act="gelu_new",
@@ -133,10 +133,10 @@ class PhiConfig(PretrainedConfig):
         tie_word_embeddings=False,
         rope_theta=10000.0,
         rope_scaling=None,
-        partial_rotary_factor=0.5,
         qk_layernorm=False,
-        bos_token_id=1,
-        eos_token_id=2,
         **kwargs,
     ):
         self.vocab_size = vocab_size

     def __init__(
         self,
+        vocab_size=50295,
+        hidden_size=2560,
+        intermediate_size=10240,
+        num_hidden_layers=32,
         num_attention_heads=32,
+        num_key_value_heads=32,
+        resid_pdrop=0.1,
         embd_pdrop=0.0,
         attention_dropout=0.0,
         hidden_act="gelu_new",
         tie_word_embeddings=False,
         rope_theta=10000.0,
         rope_scaling=None,
+        partial_rotary_factor=0.4,
         qk_layernorm=False,
+        bos_token_id=0,
+        eos_token_id=0,
         **kwargs,
     ):
         self.vocab_size = vocab_size