--- language: - en license: apache-2.0 library_name: transformers pipeline_tag: text-generation model-index: - name: speechless-zephyr-code-functionary-7b 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: 61.52 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b 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: 83.88 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b 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: 64.71 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b 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: 44.99 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b 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: 78.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b 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: 43.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-zephyr-code-functionary-7b name: Open LLM Leaderboard ---

speechless-zephyr-code-functionary-7b

[4,5,8-bit GGUF models for CPU+GPU inference](https://huggingface.co/uukuguy/speechless-zephyr-code-functionary-7b/tree/main/GGUF) This model is the one of the moloras (Mixture-of-Multi-LoRAs) experiments. Extract LoRA modules from below models (all based Mistral-7B-v0.1), each LoRA module has its own unique skills. By using multi-loras, they can be combined together statically or dynamically to form a versatile new model. - HuggingFaceH4/zephyr-7b-beta (Uncensored Model) - meetkai/functionary-small-v2.2 (Execute functions/plugins) - uukuguy/speechless-code-mistral-7b-v1.0 (Enhance Coding) The entire process is completed through the use of extract-lora, merge-lora, and lora-hub provided by multi-loras. The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks. Code: https://github.com/uukuguy/multi_loras ## LM-Evaluation-Harness [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | Metric | Value | | --- | --- | | ARC | 61.52 | | HellaSwag | 83.88 | | MMLU | 64.71 | | TruthfulQA | 44.99 | | Winogrande | 78.69 | | GSM8K | 43.82 | | Average | 62.93 | # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-zephyr-code-functionary-7b) | Metric |Value| |---------------------------------|----:| |Avg. |62.93| |AI2 Reasoning Challenge (25-Shot)|61.52| |HellaSwag (10-Shot) |83.88| |MMLU (5-Shot) |64.71| |TruthfulQA (0-shot) |44.99| |Winogrande (5-shot) |78.69| |GSM8k (5-shot) |43.82|