Instructions to use chijingLi/phi-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chijingLi/phi-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chijingLi/phi-instruct", 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("chijingLi/phi-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("chijingLi/phi-instruct", 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 chijingLi/phi-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chijingLi/phi-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": "chijingLi/phi-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/chijingLi/phi-instruct
- SGLang
How to use chijingLi/phi-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 "chijingLi/phi-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": "chijingLi/phi-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 "chijingLi/phi-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": "chijingLi/phi-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use chijingLi/phi-instruct with Docker Model Runner:
docker model run hf.co/chijingLi/phi-instruct
| # Data Summary for microsoft_Phi-3.5-mini-instruct, Phi-3.5-MoE-instruct, | |
| ## 1. General information | |
| **1.0.1 Version of the Summary:** 1.0 | |
| **1.0.2 Last update:** 10-Dec-2025 | |
| ## 1.1 Model Developer Identification | |
| **1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080 | |
| ## 1.2 Model Identification | |
| **1.2.1 Versioned model name(s):** Phi-3.5-mini-instruct, Phi-3.5-MoE-instruct, Phi-3.5-mini-instruct | |
| **1.2.2 Model release date:** August 2024 | |
| ## 1.3 Overall training data size and characteristics | |
| ### 1.3.1 Size of dataset and characteristics | |
| **1.3.1.A Text training data size:** 1 billion to 10 trillion tokens | |
| **1.3.1.B Text training data content:** Our training data includes a wide variety of sources, and is a combination of publicly available documents selected for quality, selected educational data, and code; newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); chat format supervised data covering various topics to reflect preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. | |
| **1.3.1.C Image training data size:** Not applicable | |
| **1.3.1.D Image training data content:** Not applicable | |
| **1.3.1.E Audio training data size:** Not applicable | |
| **1.3.1.F Audio training data content:** Not applicable | |
| **1.3.1.G Video training data size:** Not applicable | |
| **1.3.1.H Video training data content:** Not applicable | |
| **1.3.1.I Other training data size:** Not applicable | |
| **1.3.1.J Other training data content:** Not applicable | |
| **1.3.2 Latest date of data acquisition/collection for model training:** October 2023 | |
| **1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No | |
| **1.3.4 Date the training dataset was first used to train the model:** June 2024 | |
| **1.3.5 Rationale or purpose of data selection:** Datasets include selected publicly available documents, educational data, code, and newly created synthetic textbook-like data to emphasize reasoning-dense content such as math, coding, common sense reasoning, and general knowledge, supporting strong reasoning and instruction-following | |
| ## 2. List of data sources | |
| ### 2.1 Publicly available datasets | |
| **2.1.1 Have you used publicly available datasets to train the model?** Yes | |
| ## 2.2 Private non-publicly available datasets obtained from third parties | |
| ### 2.2.1 Datasets commercially licensed by rights holders or their representatives | |
| **2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Not applicable | |
| ### 2.2.2 Private datasets obtained from other third-parties | |
| **2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** No | |
| ## 2.3 Personal Information | |
| **2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information | |
| ## 2.4 Synthetic data | |
| **2.4.1 Was any synthetic AI-generated data used to train the model?** Yes | |
| ## 3. Data processing aspects | |
| ### 3.1 Respect of reservation of rights from text and data mining exception or limitation | |
| **3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent | |
| ## 3.2 Other information | |
| **3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities | |
| **3.2.2 Was the dataset cleaned or modified before model training?** Yes | |