Adding Evaluation Results (#1)

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	tags:
	- code
	model-index:
	- name: synapsellm-7b-mistral-v0.4-preview2
	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: 52.99
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	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: 74.54
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	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: 54.6
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	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: 53.79
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	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: 73.95
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	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: 25.7
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WebraftAI/synapsellm-7b-mistral-v0.4-preview2
	name: Open LLM Leaderboard
	---

	# SynapseLLM:

	SynapseLLM, a significant achievement by WebraftAI, represents a series of large language AI models designed to create robust, generalized, and decentralized information systems. This repository specifically houses the SynapseLLM finetuned version of Mistral. The finetuning process is conducted on a custom dataset, albeit limited in scope, focusing on code and normal question-answering scenarios. This adaptation showcases the model's versatility and applicability within specific domains, contributing to the broader landscape of AI advancements.

	## Model Details
	SynapseLLM:
	- Parameters: 7B
	- Learning rate: 2e-4
	- Adapter used: Qlora
	- Precision: float16
	- Batch size: 32
	- Maximum gradient normal: 0.3
	- Optimizer: paged_adamw_32bit
	- Warmup Ratio: 0.03
	- Step(s) (trained): 150
	- Epoch(s) (trained): 1

	### Model Description

	This is a 7b parameter, decoder only transformer based finetuned model on Chat Q/A and Code instructions. It's a preview finetune on Mistral 7B v0.1 on a sample dataset of 770k rows comprising of 361k Maths Instruct Q/A, 143k GPT-3.5 Q/A, 140k General Code, 63k Python code, and 54k General Q/A (Through GPT-4) [Each row contains one instruction and one response]. This is a full model merged and compiled with trained adapters, so you can easily load this through transformers library.


	- Developed by: WebraftAI
	- Funded by: Webraft Cloud
	- Shared by: WebraftAI
	- Model type: Decoder-only Transformer
	- Language(s): English Only
	- License: Apache 2.0
	- Finetuned from model: Mistral-7b-v0.1

	### Prompt format:
	This model follows the same prompt format as mistral instruct 7b v0.1 .The sample prompt is still given below:
	```text

	<s>[INST] Hello, how are you? [/INST]

	```

	### Example Code:
	Here's an example code using `transformers` library provided by HF.
	```python

	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview2")
	model = AutoModelForCausalLM.from_pretrained("WebraftAI/synapsellm-7b-mistral-v0.4-preview2")

	prompt= "<s>[INST] Hello! [/INST] "

	device = "cuda"

	model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
	model.to(device)

	generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
	print(tokenizer.batch_decode(generated_ids)[0])
	```

	### Model Bias:
	This model has some bias areas, discussed below:
	- Model might output factually incorrect information.
	- Model does not follow system prompts.
	- Model does not have any kind of memory, researchers can experiment feeding memory.
	- Model is trained on different datas, so it can bias information or exclaim itself as gpt model.


	# [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_WebraftAI__synapsellm-7b-mistral-v0.4-preview2)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|55.93\|
	\|AI2 Reasoning Challenge (25-Shot)\|52.99\|
	\|HellaSwag (10-Shot) \|74.54\|
	\|MMLU (5-Shot) \|54.60\|
	\|TruthfulQA (0-shot) \|53.79\|
	\|Winogrande (5-shot) \|73.95\|
	\|GSM8k (5-shot) \|25.70\|