Text Generation
Transformers
Safetensors
phi
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use rhysjones/phi-2-orange-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rhysjones/phi-2-orange-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rhysjones/phi-2-orange-v2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rhysjones/phi-2-orange-v2") model = AutoModelForCausalLM.from_pretrained("rhysjones/phi-2-orange-v2", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rhysjones/phi-2-orange-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rhysjones/phi-2-orange-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rhysjones/phi-2-orange-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rhysjones/phi-2-orange-v2
- SGLang
How to use rhysjones/phi-2-orange-v2 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 "rhysjones/phi-2-orange-v2" \ --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": "rhysjones/phi-2-orange-v2", "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 "rhysjones/phi-2-orange-v2" \ --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": "rhysjones/phi-2-orange-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rhysjones/phi-2-orange-v2 with Docker Model Runner:
docker model run hf.co/rhysjones/phi-2-orange-v2
Added evaluations
Browse files
README.md
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rhysjones__phi-2-orange-v2)
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|AI2 Reasoning Challenge (25-Shot)|61.86|
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|HellaSwag (10-Shot) |76.32|
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|MMLU (5-Shot) |55.72|
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|Winogrande (5-shot) |75.69|
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|GSM8k (5-shot) |57.62|
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# Evaluations
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[Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rhysjones__phi-2-orange-v2)
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|Average |63.67|
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|AI2 Reasoning Challenge (25-Shot)|61.86|
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|HellaSwag (10-Shot) |76.32|
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|MMLU (5-Shot) |55.72|
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|Winogrande (5-shot) |75.69|
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|GSM8k (5-shot) |57.62|
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[YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard)
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Evaluation from [mlabonne](https://huggingface.co/mlabonne)'s alternative LLM leaderboard:
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| Metric |Value|
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|Average |49.64|
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|AGIEval |34.55|
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|GPT4All |70.96|
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|TruthfulQA |54.87|
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|Bigbench |38.17|
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