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README.md

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 ---
+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
+- RLHF
+- gpt4
+- synthetic data
+- distillation
 license: apache-2.0
+base_model: mistralai/mistral-7b-v0.1
+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
+language:
+  - en
+quantized_by: Suparious
+pipeline_tag: text-generation
+model_creator: jondurbin
+model_name: bagel 7B v0.4
+inference: false
+prompt_template: '{bos}<|im_start|>{role}
+
+{text}
+
+<|im_end|>{eos}
+'
 ---
+
+# A bagel, with everything (except DPO)
+
+- Model creator: [jondurbin](https://huggingface.co/jondurbin)
+- Original model: [bagel-7b-v0.4](https://huggingface.co/jondurbin/bagel-7b-v0.4)
+
+![bagel](bagel.png)
+
+## Model Description
+
+This is the pre-DPO version of the mistral-7b model fine-tuned with https://github.com/jondurbin/bagel
+
+The DPO counterpart will be available soon, here: https://huggingface.co/jondurbin/bagel-dpo-7b-v0.4
+
+The non-DPO version is likely better for roleplay usage.
+
+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-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
+```