---
title: wikichat-v2
canonical_url: "https://www.modelscope.cn/models/LLM-Research/wikichat-v2"
md_url: "https://www.modelscope.cn/models/LLM-Research/wikichat-v2.md"
repository: LLM-Research/wikichat-v2
last_updated: 2024-09-09
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 3897
stars: 0
tags:
  - gguf
---

# wikichat-v2

> wikichat-v2 - LLM-Research 在 ModelScope 开源的模型。WikiChat-v0.2 Training in progress model to have conversations.

LLM-Research/wikichat-v2 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可。

- **Repository**: LLM-Research/wikichat-v2
- **License**: apache-2.0
- **Tasks**: text-generation
- **Tags**: gguf
- **Downloads**: 3897
- **Stars**: 0
- **Last updated**: 2024-09-09

Source: https://www.modelscope.cn/models/LLM-Research/wikichat-v2

---

# WikiChat-v0.2
Training in progress model to have conversations.

The GGUFs uploaded are full FP32 precision.

Using OpenOrca GPT-4 data + cosmopedia for some extra data + dolly15k for instruct

## Model Details:
- 83.59M parameters (83591800)
- 8 attention heads
- 40 layers
- 384 embeddings size
- 4096/8192/16384 context (please use 2/4x RoPE scaling, may train a 16k finetuned version later)
- Batch size 16
- llama.cpp (train-text-from-scratch)

## Prompt Format (Alpaca):
```
Instruction: {system}
Input: {prompt}
Response: {response}
```

Please structure your prompts in an instruct format for maximum performance.

## Training Details:
- 1x RTX 3070 8GB (Infrencing speed: 80tok/s, full GPU offload)
- 1x Ryzen 3 3700x
- 96gb RAM
- 10 iterations
- Loss Target = 2.5 to 3.0
- Approx 480 samples/1M train tokens (>0.0001 epoches)
- Training data = Refer to OpenOrca page

## Notes:

The model isn't ready yet; this is to test tokenization of OpenOrca and a balance between training speed and model size

## Example output:
```
User: What is the square root of 4?
```
```
Assistant: The square root of 4 is 2.
```