metadata

base_model: Weyaxi/Einstein-v6.1-Llama3-8B
datasets:
  - allenai/ai2_arc
  - camel-ai/physics
  - camel-ai/chemistry
  - camel-ai/biology
  - camel-ai/math
  - metaeval/reclor
  - openbookqa
  - mandyyyyii/scibench
  - derek-thomas/ScienceQA
  - TIGER-Lab/ScienceEval
  - jondurbin/airoboros-3.2
  - LDJnr/Capybara
  - Cot-Alpaca-GPT4-From-OpenHermes-2.5
  - STEM-AI-mtl/Electrical-engineering
  - knowrohit07/saraswati-stem
  - sablo/oasst2_curated
  - lmsys/lmsys-chat-1m
  - TIGER-Lab/MathInstruct
  - bigbio/med_qa
  - meta-math/MetaMathQA-40K
  - openbookqa
  - piqa
  - metaeval/reclor
  - derek-thomas/ScienceQA
  - scibench
  - sciq
  - Open-Orca/SlimOrca
  - migtissera/Synthia-v1.3
  - TIGER-Lab/ScienceEval
  - allenai/WildChat
  - microsoft/orca-math-word-problems-200k
  - openchat/openchat_sharegpt4_dataset
  - teknium/GPTeacher-General-Instruct
  - m-a-p/CodeFeedback-Filtered-Instruction
  - totally-not-an-llm/EverythingLM-data-V3
  - HuggingFaceH4/no_robots
  - OpenAssistant/oasst_top1_2023-08-25
  - WizardLM/WizardLM_evol_instruct_70k
library_name: transformers
language:
  - en
license: other
tags:
  - axolotl
  - generated_from_trainer
  - instruct
  - finetune
  - chatml
  - gpt4
  - synthetic data
  - science
  - physics
  - chemistry
  - biology
  - math
  - llama
  - llama3
  - 4-bit
  - AWQ
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
pipeline_tag: text-generation
inference: false
quantized_by: Suparious

Weyaxi/Einstein-v6.1-Llama3-8B AWQ

Model creator: Weyaxi
Original model: Einstein-v6.1-Llama3-8B

Model Summary

This model is a full fine-tuned version of meta-llama/Meta-Llama-3-8B on diverse datasets.

This model is finetuned using 8xRTX3090 + 1xRTXA6000 using axolotl.

This model's training was sponsored by sablo.ai.

How to use

Install the necessary packages

pip install --upgrade autoawq autoawq-kernels

Example Python code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/Einstein-v6.1-Llama3-8B-AWQ"
system_message = "You are Einstein-v6.1-Llama3-8B, incarnated as a powerful AI. You were created by Weyaxi."

# 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 - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code