Instructions to use microsoft/Phi-3-small-8k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-small-8k-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-small-8k-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-3-small-8k-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3-small-8k-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-small-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-small-8k-instruct

SGLang

How to use microsoft/Phi-3-small-8k-instruct 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 "microsoft/Phi-3-small-8k-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-small-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "microsoft/Phi-3-small-8k-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3-small-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-small-8k-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-small-8k-instruct
```

Phi-3-small-8k-instruct / config.json

bapatra

Enabled the AutoModelForSequenceClassification in the auto_map (#22)

69caae1 verified about 2 years ago

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	{
	"_name_or_path": "Phi-3-small-8k-instruct",
	"architectures": [
	"Phi3SmallForCausalLM"
	],
	"attention_dropout_prob": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_phi3_small.Phi3SmallConfig",
	"AutoModelForCausalLM": "modeling_phi3_small.Phi3SmallForCausalLM",
	"AutoModelForSequenceClassification": "modeling_phi3_small.Phi3SmallForSequenceClassification",
	"AutoTokenizer": "tokenization_phi3_small.Phi3SmallTokenizer"
	},
	"blocksparse_block_size": 64,
	"blocksparse_homo_head_pattern": false,
	"blocksparse_num_local_blocks": 16,
	"blocksparse_triton_kernel_block_size": 64,
	"blocksparse_vert_stride": 8,
	"bos_token_id": 100257,
	"dense_attention_every_n_layers": 2,
	"embedding_dropout_prob": 0.1,
	"eos_token_id": 100257,
	"ff_dim_multiplier": null,
	"ff_intermediate_size": 14336,
	"ffn_dropout_prob": 0.1,
	"gegelu_limit": 20.0,
	"gegelu_pad_to_256": true,
	"hidden_act": "gegelu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"layer_norm_epsilon": 1e-05,
	"max_position_embeddings": 8192,
	"model_type": "phi3small",
	"mup_attn_multiplier": 1.0,
	"mup_embedding_multiplier": 10.0,
	"mup_use_scaling": true,
	"mup_width_multiplier": 8.0,
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"pad_sequence_to_multiple_of_64": true,
	"reorder_and_upcast_attn": false,
	"rope_embedding_base": 1000000,
	"rope_position_scale": 1.0,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.38.1",
	"use_cache": true,
	"attention_bias": false,
	"vocab_size": 100352
	}