|
--- |
|
license: other |
|
library_name: transformers |
|
tags: |
|
- 4-bit |
|
- AWQ |
|
- text-generation |
|
- autotrain_compatible |
|
- endpoints_compatible |
|
pipeline_tag: text-generation |
|
inference: false |
|
quantized_by: Suparious |
|
--- |
|
# Locutusque/Llama-3-Orca-2.0-8B AWQ |
|
|
|
- Model creator: [Locutusque](https://huggingface.co/Locutusque) |
|
- Original model: [Llama-3-Orca-2.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-2.0-8B) |
|
|
|
 |
|
|
|
## Model Summary |
|
|
|
I fine-tuned llama-3 8B on mainly SlimOrca, along with other datasets to improve performance in math, coding, and writing. More data source information to come. |
|
|
|
- **Developed by:** Locutusque |
|
- **Model type:** Built with Meta Llama 3 |
|
- **Language(s) (NLP):** Many? |
|
- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE |
|
|
|
## 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/Llama-3-Orca-2.0-8B-AWQ" |
|
system_message = "You are Llama-3-Orca-2.0-8B, incarnated as a powerful AI. You were created by Locutusque." |
|
|
|
# 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 |
|
|
