base_model: cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
language:
- en
library_name: transformers
tags:
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- text-generation
- conversational
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- chatml
license: other
model_creator: cognitivecomputations
model_name: fc-dolphin-2.6-mistral-7b-dpo-laser
model_type: mistral
pipeline_tag: text-generation
inference: false
prompt_template: |
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
quantized_by: Suparious
cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser AWQ
- Model creator: cognitivecomputations
- Original model: fc-dolphin-2.6-mistral-7b-dpo-laser
by David, Fernando and Eric
Model Summary
Sponsored by: VAGO Solutions and HyperSpace.Ai
Join our Discord! https://discord.gg/cognitivecomputations
A function calling version of cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT and the novel training technique - we partially freeze the model according to a laser-like analysis (Official Paper soon) which effectively prevents the significant problem of language models forgetting previously acquired knowledge. This aspect is particularly crucial when attempting to teach the model specific skills, such as function calling.
We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
How to use
Install the necessary packages
pip install --upgrade autoawq autoawq-kernels transformers sentencepiece protobuf
Example Python code
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/fc-dolphin-2.6-mistral-7b-dpo-laser-AWQ"
system_message = "You are Dolphin, a helpful AI assistant."
# 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