starcoder wizardcoder code self-instruct distillation en license:gpl region:us
---
language:
- en
license: gpl
tags:
- starcoder
- wizardcoder
- code
- self-instruct
- distillation
inference: false
---

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# NousResearch's Redmond Hermes Coder GGML

These files are GGML format model files for [NousResearch's Redmond Hermes Coder](https://huggingface.co/NousResearch/Redmond-Hermes-Coder).

Please note that these GGMLs are **not compatible with llama.cpp, or currently with text-generation-webui**. Please see below for a list of tools known to work with these model files.

## Repositories available

* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Redmond-Hermes-Coder-GPTQ)
* [4, 5, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Redmond-Hermes-Coder-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Redmond-Hermes-Coder)

## Prompt template: Alpaca

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: PROMPT

### Response:

```

<!-- compatibility_ggml start -->
## Compatibilty

These files are **not** compatible with llama.cpp.

Currently they can be used with:
* KoboldCpp, a powerful inference engine based on llama.cpp, with good UI and GPU acceleration: [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* The ctransformers Python library, which includes LangChain support: [ctransformers](https://github.com/marella/ctransformers)
* LoLLMs WebUI which uses ctransformers: [LoLLMS WebUI](https://github.com/ParisNeo/lollms-webui)
* [rustformers' llm](https://github.com/rustformers/llm)
* The example `starcoder` binary provided with [ggml](https://github.com/ggerganov/ggml)

<!-- compatibility_ggml end -->

## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| redmond-hermes-coder.ggmlv3.q4_0.bin | q4_0 | 4 | 10.75 GB| 13.25 GB | 4-bit. |
| redmond-hermes-coder.ggmlv3.q4_1.bin | q4_1 | 4 | 11.92 GB| 14.42 GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| redmond-hermes-coder.ggmlv3.q5_0.bin | q5_0 | 5 | 13.09 GB| 15.59 GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
| redmond-hermes-coder.ggmlv3.q5_1.bin | q5_1 | 5 | 14.26 GB| 16.76 GB | 5-bit. Even higher accuracy, resource usage and slower inference. |
| redmond-hermes-coder.ggmlv3.q8_0.bin | q8_0 | 8 | 20.11 GB| 22.61 GB | 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |


**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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Thanks to the [chirper.ai](https://chirper.ai) team!

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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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Thank you to all my generous patrons and donaters!

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# Original model card: NousResearch's Redmond Hermes Coder


# 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!!
