---
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|