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Model: QuantFactory/calme-2.1-phi3.5-4b-GGUF Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: mit
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library_name: transformers
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tags:
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- chat
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- phi
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- phi3
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- phi3.5
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- finetune
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base_model: microsoft/Phi-3.5-mini-instruct
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datasets:
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- MaziyarPanahi/truthy-dpo-v0.1-axolotl
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model_name: calme-2.1-phi3.5-4b
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pipeline_tag: text-generation
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inference: false
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model_creator: MaziyarPanahi
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quantized_by: MaziyarPanahi
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model-index:
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- name: calme-2.1-phi3.5-4b
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 56.59
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 36.11
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 14.43
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 12.53
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 9.77
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 32.61
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b
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name: Open LLM Leaderboard
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---
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# QuantFactory/calme-2.1-phi3.5-4b-GGUF
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This is quantized version of [MaziyarPanahi/calme-2.1-phi3.5-4b](https://huggingface.co/MaziyarPanahi/calme-2.1-phi3.5-4b) created using llama.cpp
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# Original Model Card
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<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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# MaziyarPanahi/calme-2.1-phi3.5-4b
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This model is a fine-tuned version of the `microsoft/Phi-3.5-mini-instruct`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
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## Use Cases
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This model is suitable for a wide range of applications, including but not limited to:
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- Advanced question-answering systems
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- Intelligent chatbots and virtual assistants
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- Content generation and summarization
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- Code generation and analysis
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- Complex problem-solving and decision support
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# ⚡ Quantized GGUF
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Here are the quants: [calme-2.1-phi3.5-4b-GGUF](https://huggingface.co/MaziyarPanahi/calme-2.1-phi3.5-4b-GGUF)
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# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Coming soon!
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# Prompt Template
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This model uses `ChatML` prompt template:
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```
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<|system|>
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You are a helpful assistant.<|end|>
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<|user|>
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How to explain Internet for a medieval knight?<|end|>
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<|assistant|>
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````
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# How to use
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```python
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# Use a pipeline as a high-level helper
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from transformers import pipeline
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messages = [
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{"role": "user", "content": "Who are you?"},
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]
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pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-phi3.5-4b")
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pipe(messages)
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
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```
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# Ethical Considerations
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As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.1-phi3.5-4b)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |27.01|
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|IFEval (0-Shot) |56.59|
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|BBH (3-Shot) |36.11|
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|MATH Lvl 5 (4-Shot)|14.43|
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|GPQA (0-shot) |12.53|
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|MuSR (0-shot) | 9.77|
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|MMLU-PRO (5-shot) |32.61|
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