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---
base_model: ResplendentAI/Flora_7B
datasets:
- ResplendentAI/Synthetic_Soul_1k
inference: false
language:
- en
library_name: transformers
license: cc-by-sa-4.0
merged_models:
- jeiku/FloraBase
- jeiku/Synthetic_Soul_1k_Mistral_128
model_creator: ResplendentAI
model_name: Flora-7B
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: Suparious
tags:
- finetune
- finetuned
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- instruct
- text-generation
- conversational
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- chatml
---
# ResplendentAI/Flora-7B AWQ
- Model creator: [ResplendentAI](https://huggingface.co/ResplendentAI)
- Original model: [Flora-7B](https://huggingface.co/ResplendentAI/Flora_7B)
## Model Summary
The following YAML configuration was used to produce this model:
```yaml
merge_method: linear
models:
- model: jeiku/FloraBase+jeiku/Synthetic_Soul_1k_Mistral_128
parameters:
weight: 1
dtype: float16
```
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Flora-7B-AWQ"
system_message = "You are Flora, incarnated as a powerful AI."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
## Prompt template: ChatML
```plaintext
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Other Quant formats
exl2 and gguf by Bartowski:
- https://huggingface.co/bartowski/Flora_7B-exl2
- https://huggingface.co/bartowski/Flora_7B-GGUF
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