localmodels / Nous-Hermes-13B-ggml

Nous-Hermes-13B ggml

From: https://huggingface.co/NousResearch/Nous-Hermes-13b

Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0

Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48.

k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K

Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387.

Provided Files

Model Card: Nous-Hermes-13b

Model Description

Nous-Hermes-13b 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. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks.

This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.

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from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), CodeAlpaca, Evol_Instruct Uncensored, GPT4-LLM, and Unnatural Instructions.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' 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 between Teknium, Karan4D, Nous Research, Huemin Art, and Redmond AI.

Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.

Special mention goes to @winglian, @erhartford, and @main_horse for assisting in some of the training issues.

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:

Benchmark Results

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.4915|±  |0.0146|
|             |       |acc_norm|0.5085|±  |0.0146|
|arc_easy     |      0|acc     |0.7769|±  |0.0085|
|             |       |acc_norm|0.7424|±  |0.0090|
|boolq        |      1|acc     |0.7948|±  |0.0071|
|hellaswag    |      0|acc     |0.6143|±  |0.0049|
|             |       |acc_norm|0.8000|±  |0.0040|
|openbookqa   |      0|acc     |0.3560|±  |0.0214|
|             |       |acc_norm|0.4640|±  |0.0223|
|piqa         |      0|acc     |0.7965|±  |0.0094|
|             |       |acc_norm|0.7889|±  |0.0095|
|winogrande   |      0|acc     |0.7190|±  |0.0126|