ExLLaMA V2 quant of Yarn-Mistral-7b-128k-4.0bpw-h6-exl2

README.md

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+---
+datasets:
+- emozilla/yarn-train-tokenized-16k-mistral
+metrics:
+- perplexity
+library_name: transformers
+---
+
+# Model Card: Nous-Yarn-Mistral-7b-128k
+
+[Preprint (arXiv)](https://arxiv.org/abs/2309.00071)  
+[GitHub](https://github.com/jquesnelle/yarn)
+![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png)
+
+## Model Description
+
+Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method.
+It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window.
+
+To use, pass `trust_remote_code=True` when loading the model, for example
+
+```python
+model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k",
+  use_flash_attention_2=True,
+  torch_dtype=torch.bfloat16,
+  device_map="auto",
+  trust_remote_code=True)
+```
+
+In addition you will need to use the latest version of `transformers` (until 4.35 comes out)
+```sh
+pip install git+https://github.com/huggingface/transformers
+```
+
+## Benchmarks
+
+Long context benchmarks:
+| Model | Context Window | 8k PPL | 16k PPL | 32k PPL | 64k PPL | 128k PPL |
+|-------|---------------:|------:|----------:|-----:|-----:|------------:|
+| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 2.96 | - | - | - | - |
+| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 3.04 | 2.65 | 2.44 | 2.20 | - |
+| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 3.08 | 2.68 | 2.47 | 2.24 | 2.19 |
+
+Short context benchmarks showing that quality degradation is minimal:
+| Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
+|-------|---------------:|------:|----------:|-----:|------------:|
+| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 59.98 | 83.31 | 64.16 | 42.15 |
+| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 59.38 | 81.21 | 61.32 | 42.50 |
+| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 58.87 | 80.58 | 60.64 | 42.46 |
+
+## Collaborators
+
+ - [bloc97](https://github.com/bloc97): Methods, paper and evals
+ - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals
+ - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training
+ - [honglu2875](https://github.com/honglu2875): Paper and evals
+
+The authors would like to thank LAION AI for their support of compute for this model.
+It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.

added_tokens.json

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+{
+  "</s>": 2,
+  "<s>": 1,
+  "<unk>": 0
+}

config.json

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+{
+  "_name_or_path": "NousResearch/Yarn-Mistral-7b-128k",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_mistral.MistralConfig",
+    "AutoModelForCausalLM": "modeling_mistral_yarn.MistralForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "max_sequence_length": 131072,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 16.0,
+    "finetuned": true,
+    "original_max_position_embeddings": 8192,
+    "type": "yarn"
+  },
+  "rope_theta": 10000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.35.0.dev0"
+}

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+size 3862581248

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