Instructions to use win10/phi3.5-pro-10-08 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use win10/phi3.5-pro-10-08 with Transformers:

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

pipe = pipeline("text-generation", model="win10/phi3.5-pro-10-08", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("win10/phi3.5-pro-10-08", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("win10/phi3.5-pro-10-08", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use win10/phi3.5-pro-10-08 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "win10/phi3.5-pro-10-08"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "win10/phi3.5-pro-10-08",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/win10/phi3.5-pro-10-08

SGLang

How to use win10/phi3.5-pro-10-08 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 "win10/phi3.5-pro-10-08" \
    --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": "win10/phi3.5-pro-10-08",
		"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 "win10/phi3.5-pro-10-08" \
        --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": "win10/phi3.5-pro-10-08",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use win10/phi3.5-pro-10-08 with Docker Model Runner:
```
docker model run hf.co/win10/phi3.5-pro-10-08
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

phi3.5-pro-10-08

This model is a fine-tuned version of E:\mergekit\phi3.5-pro on the Magpie_Qwen2_Pro_300k and the Magpie_Llama_3.1_Pro_MT_300k datasets. It achieves the following results on the evaluation set:

Loss: 0.8538

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.7784	0.0080	100	0.8538
0.8605	0.0160	200	0.8538
0.8386	0.0240	300	0.8538
0.8201	0.0320	400	0.8538
0.8661	0.0400	500	0.8538
0.776	0.0480	600	0.8538
0.8377	0.0561	700	0.8538
0.8541	0.0641	800	0.8538
0.799	0.0721	900	0.8538
0.8146	0.0801	1000	0.8538

Framework versions

Transformers 4.45.0
Pytorch 2.4.0+cu124
Datasets 2.21.0
Tokenizers 0.20.0

Downloads last month: 4

Safetensors

Model size

5B params

Tensor type

F32

F16

Model tree for win10/phi3.5-pro-10-08

Quantizations

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