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Growth Rate:
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Sponsored by
APIMart
solidrust / dolphin-2.8-experiment26-7b-AWQ
Model's Last Updated: September 03 2024
text-generation
Introduction of dolphin-2.8-experiment26-7b-AWQ
Model Details of dolphin-2.8-experiment26-7b-AWQ
cognitivecomputations/dolphin-2.8-experiment26-7b AWQ
- Model creator: cognitivecomputations
- Original model: dolphin-2.8-experiment26-7b
Model Summary
Sponsored by MassedCompute
Discord https://discord.gg/cognitivecomputations
This model is based on Experiment-26 by Yam Peleg .
The base model has 16k context
This Dolphin is really good at coding, @ehartford trained this with a lot of coding data.
It took 3 days to train 3 epochs on 7x A6000s using qlora on Axolotl
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/dolphin-2.8-experiment26-7b-AWQ"
system_message = "You are Hercules, 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 - 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
Prompt template: ChatML
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Runs of solidrust dolphin-2.8-experiment26-7b-AWQ on huggingface.co
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24-hour runs
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More Information About dolphin-2.8-experiment26-7b-AWQ huggingface.co Model
Url of dolphin-2.8-experiment26-7b-AWQ
dolphin-2.8-experiment26-7b-AWQ huggingface.co Url
Provider of dolphin-2.8-experiment26-7b-AWQ huggingface.co
Other API from solidrust
Updated:September 03 2024
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