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phi-2-orange-v2 - GGUF

• Model creator: https://huggingface.co/rhysjones/
• Original model: https://huggingface.co/rhysjones/phi-2-orange-v2/

Name	Quant method	Size
phi-2-orange-v2.Q2_K.gguf	Q2_K	1.03GB
phi-2-orange-v2.IQ3_XS.gguf	IQ3_XS	1.12GB
phi-2-orange-v2.IQ3_S.gguf	IQ3_S	1.16GB
phi-2-orange-v2.Q3_K_S.gguf	Q3_K_S	1.16GB
phi-2-orange-v2.IQ3_M.gguf	IQ3_M	1.23GB
phi-2-orange-v2.Q3_K.gguf	Q3_K	1.33GB
phi-2-orange-v2.Q3_K_M.gguf	Q3_K_M	1.33GB
phi-2-orange-v2.Q3_K_L.gguf	Q3_K_L	1.47GB
phi-2-orange-v2.IQ4_XS.gguf	IQ4_XS	1.43GB
phi-2-orange-v2.Q4_0.gguf	Q4_0	1.49GB
phi-2-orange-v2.IQ4_NL.gguf	IQ4_NL	1.5GB
phi-2-orange-v2.Q4_K_S.gguf	Q4_K_S	1.51GB
phi-2-orange-v2.Q4_K.gguf	Q4_K	1.62GB
phi-2-orange-v2.Q4_K_M.gguf	Q4_K_M	1.62GB
phi-2-orange-v2.Q4_1.gguf	Q4_1	1.65GB
phi-2-orange-v2.Q5_0.gguf	Q5_0	1.8GB
phi-2-orange-v2.Q5_K_S.gguf	Q5_K_S	1.8GB
phi-2-orange-v2.Q5_K.gguf	Q5_K	1.87GB
phi-2-orange-v2.Q5_K_M.gguf	Q5_K_M	1.87GB
phi-2-orange-v2.Q5_1.gguf	Q5_1	1.95GB
phi-2-orange-v2.Q6_K.gguf	Q6_K	2.13GB
phi-2-orange-v2.Q8_0.gguf	Q8_0	2.75GB

Original model description:

license: mit datasets:

• Open-Orca/SlimOrca-Dedup
• migtissera/Synthia-v1.3
• LDJnr/Verified-Camel
• LDJnr/Pure-Dove
• LDJnr/Capybara
• meta-math/MetaMathQA
• Intel/orca_dpo_pairs
• argilla/ultrafeedback-binarized-preferences-cleaned widget:
  • example_title: "Example interaction" text: "Why is the sky blue?" inference: parameters: do_sample: True temperature: 0.1 model-index:
• name: phi-2-orange-v2 results:
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 61.86 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 76.32 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 55.72 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 54.84 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 75.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 57.62 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rhysjones/phi-2-orange-v2 name: Open LLM Leaderboard

---

Phi-2 Orange Version 2

A two-step finetune of Phi-2, with a bit more zest.

This is an improved version of the original Phi-2-Orange that uses an updated training process on the same datasets.

It also uses the latest updated model from Microsoft's Phi-2, making it directly usable within Hugging Face's Transformers library (without the need for trust remote code).

Prompt Format

Phi-2 Orange v2 uses ChatML as the prompt format.
(Update 12th March 2024: fixed eos_token issue)

It's recommended to always prompt with a system instruction (use whatever system prompt you like):

<|im_start|>system
You are a helpful assistant for Python which outputs in Markdown format.<|im_end|>
<|im_start|>user
Write a function to calculate the Fibonacci sequence<|im_end|>
<|im_start|>assistant

For example, if you find the model's output to be overly verbose, instruct it to be short and concise:

<|im_start|>system
You are a helpful assistant. Be short and direct in your answers.<|im_end|>
<|im_start|>user
Was Tom Hanks in the movie Forrest Gump? If so, who did he play and give details of the plot.<|im_end|>
<|im_start|>assistant

Evaluations

Open LLM Leaderboard Evaluation Results
Detailed results can be found here

Metric	Value
Average	63.67
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	76.32
MMLU (5-Shot)	55.72
TruthfulQA (0-shot)	54.84
Winogrande (5-shot)	75.69
GSM8k (5-shot)	57.62

YALL - Yet Another LLM Leaderboard
Evaluation from mlabonne's alternative LLM leaderboard:

Metric	Value
Average	49.64
AGIEval	34.55
GPT4All	70.96
TruthfulQA	54.87
Bigbench	38.17

Limitations

This model shares the same limitations as the underlying Phi-2 model, details of which are found here.