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bagel-8b-v1.0-AWQ

Text Generation
Transformers
Safetensors
llama
4-bit precision
AWQ
endpoints_compatible - llama-3 - bagel
conversational
text-generation-inference
awq
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bagel-8b-v1.0-AWQ / README.md
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metadata
license: other
license_name: llama3
license_link: https://huggingface.co./meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
base_model: jondurbin/bagel-8b-v1.0
library_name: transformers
tags:
  - 4-bit
  - AWQ
  - text-generation
  - autotrain_compatible
  - endpoints_compatible - llama-3 - bagel
datasets:
  - ai2_arc
  - allenai/ultrafeedback_binarized_cleaned
  - argilla/distilabel-intel-orca-dpo-pairs
  - jondurbin/airoboros-3.2
  - codeparrot/apps
  - facebook/belebele
  - bluemoon-fandom-1-1-rp-cleaned
  - boolq
  - camel-ai/biology
  - camel-ai/chemistry
  - camel-ai/math
  - camel-ai/physics
  - jondurbin/contextual-dpo-v0.1
  - jondurbin/gutenberg-dpo-v0.1
  - jondurbin/py-dpo-v0.1
  - jondurbin/truthy-dpo-v0.1
  - LDJnr/Capybara
  - jondurbin/cinematika-v0.1
  - WizardLM/WizardLM_evol_instruct_70k
  - glaiveai/glaive-function-calling-v2
  - jondurbin/gutenberg-dpo-v0.1
  - grimulkan/LimaRP-augmented
  - lmsys/lmsys-chat-1m
  - ParisNeo/lollms_aware_dataset
  - TIGER-Lab/MathInstruct
  - Muennighoff/natural-instructions
  - openbookqa
  - kingbri/PIPPA-shareGPT
  - piqa
  - Vezora/Tested-22k-Python-Alpaca
  - ropes
  - cakiki/rosetta-code
  - Open-Orca/SlimOrca
  - b-mc2/sql-create-context
  - squad_v2
  - mattpscott/airoboros-summarization
  - migtissera/Synthia-v1.3
  - unalignment/toxic-dpo-v0.2
  - WhiteRabbitNeo/WRN-Chapter-1
  - WhiteRabbitNeo/WRN-Chapter-2
  - winogrande
pipeline_tag: text-generation
inference: false
quantized_by: Suparious

jondurbin/bagel-8b-v1.0 AWQ

bagel

Model Summary

The name of this model is "llama-3-bagel-8b-v1.0" and it was built with llama-3 from Meta.

This is a fine-tune of llama-3-8b using the bagel dataset, but instead of 4 prompt formats it's standardized on a single format - llama-3 instruct.

See bagel for additional details on the datasets.

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/bagel-8b-v1.0-AWQ"
system_message = "You are bagel-8b-v1.0, incarnated as a powerful AI. You were created by jondurbin."

# 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: