Adding Evaluation Results (#1)

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	---
	license: apache-2.0
	tags:
	- finetuned
	pipeline_tag: text-generation
	inference: false
	model-index:
	- name: Mistral-7B-Instruct-v0.2-attention-sparsity-30
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 62.97
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 84.71
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 60.49
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 67.49
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 77.98
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 39.42
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-30
	name: Open LLM Leaderboard
	---

	# Model Card for Mistral-7B-Instruct-v0.2

	The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).

	For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).

	## Instruction format

	In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

	E.g.
	```
	text = "<s>[INST] What is your favourite condiment? [/INST]"
	"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
	"[INST] Do you have mayonnaise recipes? [/INST]"
	```

	This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	device = "cuda" # the device to load the model onto

	model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
	tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")

	messages = [
	{"role": "user", "content": "What is your favourite condiment?"},
	{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
	{"role": "user", "content": "Do you have mayonnaise recipes?"}
	]

	encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

	model_inputs = encodeds.to(device)
	model.to(device)

	generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
	decoded = tokenizer.batch_decode(generated_ids)
	print(decoded[0])
	```

	## Model Architecture
	This instruction model is based on Mistral-7B-v0.1, 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:
	```
	Traceback (most recent call last):
	File "", line 1, in
	File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
	config, kwargs = AutoConfig.from_pretrained(
	File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
	config_class = CONFIG_MAPPING[config_dict["model_type"]]
	File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
	raise KeyError(key)
	KeyError: 'mistral'
	```

	Installing transformers from source should solve the issue
	pip install git+https://github.com/huggingface/transformers

	This should not be required after transformers-v4.33.4.

	## Limitations

	The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
	It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
	make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

	## The Mistral AI Team

	Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_notadib__Mistral-7B-Instruct-v0.2-attention-sparsity-30)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|65.51\|
	\|AI2 Reasoning Challenge (25-Shot)\|62.97\|
	\|HellaSwag (10-Shot) \|84.71\|
	\|MMLU (5-Shot) \|60.49\|
	\|TruthfulQA (0-shot) \|67.49\|
	\|Winogrande (5-shot) \|77.98\|
	\|GSM8k (5-shot) \|39.42\|