transformers safetensors mistral text-generation finetuned quantized 4-bit AWQ pytorch instruct conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us finetune chatml DPO RLHF gpt4 synthetic data distillation en dataset:allenai/ai2_arc dataset:allenai/ultrafeedback_binarized_cleaned dataset:argilla/distilabel-intel-orca-dpo-pairs dataset:jondurbin/airoboros-3.2 dataset:codeparrot/apps dataset:facebook/belebele dataset:bluemoon-fandom-1-1-rp-cleaned dataset:boolq dataset:camel-ai/biology dataset:camel-ai/chemistry dataset:camel-ai/math dataset:camel-ai/physics dataset:jondurbin/contextual-dpo-v0.1 dataset:jondurbin/gutenberg-dpo-v0.1 dataset:jondurbin/py-dpo-v0.1 dataset:jondurbin/truthy-dpo-v0.1 dataset:LDJnr/Capybara dataset:jondurbin/cinematika-v0.1 dataset:WizardLM/WizardLM_evol_instruct_70k dataset:glaiveai/glaive-function-calling-v2 dataset:grimulkan/LimaRP-augmented dataset:lmsys/lmsys-chat-1m dataset:ParisNeo/lollms_aware_dataset dataset:TIGER-Lab/MathInstruct dataset:Muennighoff/natural-instructions dataset:openbookqa dataset:kingbri/PIPPA-shareGPT dataset:piqa dataset:Vezora/Tested-22k-Python-Alpaca dataset:ropes dataset:cakiki/rosetta-code dataset:Open-Orca/SlimOrca dataset:b-mc2/sql-create-context dataset:squad_v2 dataset:mattpscott/airoboros-summarization dataset:migtissera/Synthia-v1.3 dataset:unalignment/toxic-dpo-v0.2 dataset:WhiteRabbitNeo/WRN-Chapter-1 dataset:WhiteRabbitNeo/WRN-Chapter-2 dataset:winogrande base_model:mistralai/Mistral-7B-v0.1 awq
---
language:
- en
license: apache-2.0
tags:
- finetuned
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- instruct
- text-generation
- conversational
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
datasets:
- allenai/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
model_name: bagel 7B v0.4 DPO
base_model: mistralai/Mistral-7B-v0.1
quantized_by: Suparious
pipeline_tag: text-generation
model_creator: jondurbin
inference: false
prompt_template: '{bos}<|im_start|>{role}

  {text}

  <|im_end|>{eos} '
---

# A bagel, with everything

- Model creator: [jondurbin](https://huggingface.co/jondurbin)
- Original model: [bagel-dpo-7b-v0.4](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.4)

![bagel](bagel.png)

## Model Description

This is a fine-tune of mistral-7b-v0.1, which underwent additional fine-tuning using direct preference optimization (DPO).

See [bagel](https://github.com/jondurbin/bagel) for additional details on the datasets.

The non-DPO version is available [here](https://huggingface.co/solidrust/bagel-7b-v0.4-AWQ), and is likely superior for roleplay.

Compute generously provided by [MassedCompute](https://massedcompute.com/?utm_source=huggingface&utm_creative_format=model_card&utm_content=creator_jon)

## 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/bagel-dpo-7b-v0.4-AWQ"
system_message = "You are Bagel, incarnated a powerful AI with everything."

# 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

## Prompt template: ChatML

```plaintext
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
