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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- open-source |
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- code |
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- math |
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- chemistry |
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- biology |
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- transformers |
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- mistral |
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- text-generation-inference |
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- question-answering |
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- quantized |
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- 4-bit |
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- AWQ |
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- text-generation |
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- autotrain_compatible |
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- endpoints_compatible |
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- chatml |
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datasets: |
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- Locutusque/OpenCerebrum-dpo |
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model_creator: Locutusque |
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model_name: OpenCerebrum-1.0-7b-DPO |
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model_type: mistral |
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pipeline_tag: text-generation |
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inference: false |
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prompt_template: '<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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' |
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quantized_by: Suparious |
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--- |
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# Locutusque/OpenCerebrum-1.0-7b-DPO AWQ |
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- Model creator: [Locutusque](https://huggingface.co/Locutusque) |
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- Original model: [OpenCerebrum-1.0-7b-DPO](https://huggingface.co/Locutusque/OpenCerebrum-1.0-7b-DPO) |
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## Model Summary |
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OpenCerebrum-1.0-7B-DPO is an open-source language model fine-tuned from the alpindale/Mistral-7B-v0.2-hf base model on a diverse dataset aimed at replicating capabilities of Aether Research's proprietary Cerebrum model. |
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The model was fine-tuned on approximately 21,000 examples across 6 datasets spanning coding, math, science, reasoning, and general instruction-following. The goal was to assemble public datasets that could help the model achieve strong performance on benchmarks where Cerebrum excels. |
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I used the ChatML prompt format to train this model. |
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- **Base Model:** alpindale/Mistral-7B-v0.2-hf |
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- **Parameters:** 7 billion |
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- **Fine-Tuning Dataset Size:** ~21,000 examples |
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- **Fine-Tuning Data:** Amalgamation of 6 public datasets |
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- **Language:** English |
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- **License:** Apache 2.0 |
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## How to use |
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### Install the necessary packages |
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```bash |
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pip install --upgrade autoawq autoawq-kernels |
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``` |
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### Example Python code |
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```python |
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from awq import AutoAWQForCausalLM |
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from transformers import AutoTokenizer, TextStreamer |
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model_path = "solidrust/OpenCerebrum-1.0-7b-DPO-AWQ" |
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system_message = "You are Cerebrum, incarnated as a powerful AI." |
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# Load model |
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model = AutoAWQForCausalLM.from_quantized(model_path, |
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fuse_layers=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, |
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trust_remote_code=True) |
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streamer = TextStreamer(tokenizer, |
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skip_prompt=True, |
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skip_special_tokens=True) |
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# Convert prompt to tokens |
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prompt_template = """\ |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant""" |
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prompt = "You're standing on the surface of the Earth. "\ |
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"You walk one mile south, one mile west and one mile north. "\ |
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"You end up exactly where you started. Where are you?" |
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tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), |
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return_tensors='pt').input_ids.cuda() |
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# Generate output |
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generation_output = model.generate(tokens, |
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streamer=streamer, |
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max_new_tokens=512) |
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``` |
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### About AWQ |
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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. |
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AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. |
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It is supported by: |
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- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ |
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- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. |
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) |
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- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers |
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code |
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## Prompt template: ChatML |
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```plaintext |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
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