初始化项目，由ModelHub XC社区提供模型
Model: damerajee/Gaja-v2.00 Source: Original Platform
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ModelHub XC
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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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- hi
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license: llama2
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library_name: transformers
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tags:
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- hindi
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- 'english '
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- Bilingual
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datasets:
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- sarvamai/samvaad-hi-v1
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pipeline_tag: text-generation
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model-index:
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- name: Gaja-v2.00
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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: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 51.79
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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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: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 75.79
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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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 (5-Shot)
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type: cais/mmlu
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config: all
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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: 40.69
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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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: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 41.5
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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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: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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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: 71.9
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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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: GSM8k (5-shot)
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type: gsm8k
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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: 0.23
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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name: Open LLM Leaderboard
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---
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# Model
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# 🐘 Gaja
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Gaja is a Hindi/Hinglish chat model, initially trained on SarvamAI's OpenHathi model and further fine-tuned for conversational interactions.
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6487239cca30096ea9f52115/sLgskU1h2Tih_bJo0ktYR.jpeg" width="600" alt="Image">
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# Additional Information
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- It outperforms Airavata, AI4Bharat's chat version, on Huggingface OpenLLM benchmark suite.
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- It was fine-tuned on only 5k samples
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# Inference
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hey guys thanks to Bhabha AI, you guys can finally try my [model](https://www.indic.chat/)
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# Additional Information
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- The code for this can be found in The github code - [Github](https://github.com/dame-cell/Gaja)
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# 💬 Prompt template
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```
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<|im_start|>user
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{}<|im_end|>
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<|im_start|>assistant
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{}<|im_end|>
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```
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# 😎 Features:
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- Language Support: Gaja is designed to understand and generate responses in both Hindi and Hinglish, catering to a diverse range of users.
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- Base Model: Built upon SarvamAI's OpenHathi model, Gaja inherits its foundational capabilities while being optimized for conversational tasks.
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- Fine-tuning: Gaja has undergone fine-tuning specifically for chat-based interactions, enhancing its ability to engage in meaningful conversations with users.
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- Experimental Platform: With its flexibility and adaptability, Gaja serves as a valuable platform for conducting experiments and exploring innovative approaches to chatbot development.
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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_damerajee__Gaja-v2.00)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |46.98|
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|AI2 Reasoning Challenge (25-Shot)|51.79|
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|HellaSwag (10-Shot) |75.79|
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|MMLU (5-Shot) |40.69|
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|TruthfulQA (0-shot) |41.50|
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|Winogrande (5-shot) |71.90|
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|GSM8k (5-shot) | 0.23|
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