An Analysis of Chinese LLM Censorship and Bias with Qwen 2 Instruct
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leonardlin
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My weekened project ended up being doing some testing between torchtune, axolotl, and unsloth. I *think* it's a 1:1 comparison of what LoRA fine-tuning performance looks like between the different hardware I have in my dev boxes (4090, 3090, 7900 XTX, W7900) with a few other interesting tidbits.
Tonight I wrote up a WandB report (the panel editor is super broken in Firefox 😔) that sums up some of the more interesting bits from the results: https://wandb.ai/augmxnt/train-bench/reports/torchtune-vs-axolotl-vs-unsloth-Trainer-Comparison--Vmlldzo4MzU3NTAx
Tonight I wrote up a WandB report (the panel editor is super broken in Firefox 😔) that sums up some of the more interesting bits from the results: https://wandb.ai/augmxnt/train-bench/reports/torchtune-vs-axolotl-vs-unsloth-Trainer-Comparison--Vmlldzo4MzU3NTAx
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Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis
I also have an accompanying model and dataset (and codebase) for those curious to poke around:
* augmxnt/Qwen2-7B-Instruct-deccp
* augmxnt/deccp
I also have an accompanying model and dataset (and codebase) for those curious to poke around:
* augmxnt/Qwen2-7B-Instruct-deccp
* augmxnt/deccp
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LLM in a flash: Efficient Large Language Model Inference with Limited Memory
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
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QuIP: 2-Bit Quantization of Large Language Models With Guarantees
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SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
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OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
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AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
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