| | --- |
| | license: gpl |
| | language: |
| | - en |
| | tags: |
| | - starcoder |
| | - wizardcoder |
| | - code |
| | - self-instruct |
| | - distillation |
| | --- |
| | |
| | # Model Card: Redmond-Hermes-Coder 15B |
| |
|
| | ## Model Description |
| |
|
| | Redmond-Hermes-Coder 15B is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. |
| |
|
| | This model was trained with a WizardCoder base, which itself uses a StarCoder base model. |
| |
|
| | The model is truly great at code, but, it does come with a tradeoff though. While far better at code than the original Nous-Hermes built on Llama, it is worse than WizardCoder at pure code benchmarks, like HumanEval. |
| |
|
| | It comes in at 39% on HumanEval, with WizardCoder at 57%. This is a preliminary experiment, and we are exploring improvements now. |
| |
|
| | However, it does seem better at non-code than WizardCoder on a variety of things, including writing tasks. |
| |
|
| | ## Model Training |
| |
|
| | The model was trained almost entirely on synthetic GPT-4 outputs. This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), CodeAlpaca, Evol_Instruct Uncensored, GPT4-LLM, and Unnatural Instructions. |
| | |
| | Additional data inputs came from Camel-AI's Biology/Physics/Chemistry and Math Datasets, Airoboros' (v1) GPT-4 Dataset, and more from CodeAlpaca. The total volume of data encompassed over 300,000 instructions. |
| | |
| | ## Collaborators |
| | The model fine-tuning and the datasets were a collaboration of efforts and resources from members of Nous Research, includingTeknium, Karan4D, Huemin Art, and Redmond AI's generous compute grants. |
| | |
| | Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. |
| | |
| | Among the contributors of datasets, GPTeacher was made available by Teknium, Wizard LM by nlpxucan, and the Nous Research Instruct Dataset was provided by Karan4D and HueminArt. |
| | The GPT4-LLM and Unnatural Instructions were provided by Microsoft, Airoboros dataset by jondurbin, Camel-AI datasets are from Camel-AI, and CodeAlpaca dataset by Sahil 2801. |
| | If anyone was left out, please open a thread in the community tab. |
| | |
| | ## Prompt Format |
| | |
| | The model follows the Alpaca prompt format: |
| | ``` |
| | ### Instruction: |
| | |
| | ### Response: |
| | ``` |
| | |
| | or |
| | |
| | ``` |
| | ### Instruction: |
| | |
| | ### Input: |
| | |
| | ### Response: |
| | ``` |
| | |
| | ## Resources for Applied Use Cases: |
| | For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord |
| | For an example of a roleplaying discord bot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot |
| | |
| | ## Future Plans |
| | The model is currently being uploaded in FP16 format, and there are plans to convert the model to GGML and GPTQ 4bit quantizations. The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. We will try to get in discussions to get the model included in the GPT4All. |
| | |
| | ## Benchmark Results |
| | ``` |
| | HumanEval: 39% |
| | | Task |Version| Metric |Value | |Stderr| |
| | |------------------------------------------------|------:|---------------------|-----:|---|-----:| |
| | |arc_challenge | 0|acc |0.2858|± |0.0132| |
| | | | |acc_norm |0.3148|± |0.0136| |
| | |arc_easy | 0|acc |0.5349|± |0.0102| |
| | | | |acc_norm |0.5097|± |0.0103| |
| | |bigbench_causal_judgement | 0|multiple_choice_grade|0.5158|± |0.0364| |
| | |bigbench_date_understanding | 0|multiple_choice_grade|0.5230|± |0.0260| |
| | |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3295|± |0.0293| |
| | |bigbench_geometric_shapes | 0|multiple_choice_grade|0.1003|± |0.0159| |
| | | | |exact_str_match |0.0000|± |0.0000| |
| | |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2260|± |0.0187| |
| | |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1957|± |0.0150| |
| | |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.3733|± |0.0280| |
| | |bigbench_movie_recommendation | 0|multiple_choice_grade|0.3200|± |0.0209| |
| | |bigbench_navigate | 0|multiple_choice_grade|0.4830|± |0.0158| |
| | |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.4150|± |0.0110| |
| | |bigbench_ruin_names | 0|multiple_choice_grade|0.2143|± |0.0194| |
| | |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2926|± |0.0144| |
| | |bigbench_snarks | 0|multiple_choice_grade|0.5249|± |0.0372| |
| | |bigbench_sports_understanding | 0|multiple_choice_grade|0.4817|± |0.0159| |
| | |bigbench_temporal_sequences | 0|multiple_choice_grade|0.2700|± |0.0140| |
| | |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.1864|± |0.0110| |
| | |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1349|± |0.0082| |
| | |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.3733|± |0.0280| |
| | |boolq | 1|acc |0.5498|± |0.0087| |
| | |hellaswag | 0|acc |0.3814|± |0.0048| |
| | | | |acc_norm |0.4677|± |0.0050| |
| | |openbookqa | 0|acc |0.1960|± |0.0178| |
| | | | |acc_norm |0.3100|± |0.0207| |
| | |piqa | 0|acc |0.6600|± |0.0111| |
| | | | |acc_norm |0.6610|± |0.0110| |
| | |winogrande | 0|acc |0.5343|± |0.0140| |
| | ``` |
| | |
| | ## Model Usage |
| | The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. |
| | |
| | Compute provided by our project sponsor Redmond AI, thank you!! |
