99 lines
3.6 KiB
Markdown
99 lines
3.6 KiB
Markdown
---
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license: llama3.1
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base_model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b
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library_name: transformers
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tags:
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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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- generated_from_trainer
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datasets:
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- cognitivecomputations/Dolphin-2.9
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- m-a-p/CodeFeedback-Filtered-Instruction
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- cognitivecomputations/dolphin-coder
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- cognitivecomputations/samantha-data
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- microsoft/orca-math-word-problems-200k
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- mlabonne/FineTome-100k
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- arcee/agent_data
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- PawanKrd/math-gpt-4o-200k
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- cognitivecomputations/SystemChat-2.0
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pipeline_tag: text-generation
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inference: false
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quantized_by: Suparious
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---
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# cognitivecomputations/dolphin-2.9.4-llama3.1-8b AWQ
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- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
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- Original model: [dolphin-2.9.4-llama3.1-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b)
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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Curated and trained by Eric Hartford and Cognitive Computations.
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This model is based on Meta Llama 3.1 8b, and is governed by the Llama 3.1 license.
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The base model has 128K context, and our finetuning used 8192 sequence length.
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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/dolphin-2.9.4-llama3.1-8b-AWQ"
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system_message = "You are dolphin-2.9.4-llama3.1-8b, incarnated as a powerful AI. You were created by cognitivecomputations."
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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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