Quantization made by Richard Erkhov.
speechless-mistral-dolphin-orca-platypus-samantha-7b - GGUF
- Model creator: https://huggingface.co/uukuguy/
- Original model: https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b/
Original model description:
language: - en library_name: transformers pipeline_tag: text-generation datasets: - jondurbin/airoboros-2.2.1 - Open-Orca/OpenOrca - garage-bAInd/Open-Platypus - ehartford/samantha-data tags: - llama-2 - code license: llama2 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 34.146 verified: false
speechless-mistral-dolphin-orca-platypus-samantha-7b
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
This model is a merge of ehartford/dolphin-2.1-mistral-7b, Open-Orca/Mistral-7B-OpenOrca, bhenrym14/mistral-7b-platypus-fp16 and ehartford/samantha-1.2-mistral-7b.
I'm very sorry for giving such a long and peculiar name. Originally, it was just my lazy behavior during the process of making models to easily distinguish various model and dataset combinations. I didn't expect the previous model (Thebloke GPTQ Version) to be so popular. This time, based on some guys's request, I am releasing a model based on Mistral, and I have also inherited the style of the super long name along with it. Welcome to try the model, please refrain from harsh criticism if you don't like it.
Code: https://github.com/uukuguy/speechless
HumanEval
| Metric | Value |
|---|---|
| humaneval-python | 34.146 |
CodeLlama-34B-Python: 53.29
CodeLlama-34B-Instruct: 50.79
CodeLlama-13B-Instruct: 50.6
CodeLlama-34B: 45.11
CodeLlama-13B-Python: 42.89
CodeLlama-13B: 35.07
Mistral-7B-v0.1: 30.488
LM-Evaluation-Harness
| Metric | Value |
|---|---|
| ARC | 64.33 |
| HellaSwag | 84.4 |
| MMLU | 63.72 |
| TruthfulQA | 52.52 |
| Winogrande | 78.37 |
| GSM8K | 21.38 |
| DROP | 8.66 |
| Average | 53.34 |
Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our paper and release blog post.
Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Troubleshooting
- If you see the following error:
KeyError: 'mistral' - Or:
NotImplementedError: Cannot copy out of meta tensor; no data!
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.`
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 53.34 |
| ARC (25-shot) | 64.33 |
| HellaSwag (10-shot) | 84.4 |
| MMLU (5-shot) | 63.72 |
| TruthfulQA (0-shot) | 52.52 |
| Winogrande (5-shot) | 78.37 |
| GSM8K (5-shot) | 21.38 |
| DROP (3-shot) | 8.66 |
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