jrell/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller
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jrell/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller:Launch configurations updated a model 3 days ago
jrell/Qwen3.8-27B-i1-IQ4_XS-GGUF-Smaller published a model 4 days ago
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Launch configurations
3
#3 opened 1 day ago
by 
raragi
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📢 New Research Alert: Making Language Models Smaller & Smarter!
Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance.
The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena.
🔑 Key Findings:
• 77% parameter reduction.
• Maintained model capabilities.
• Improved generalization.
Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT
Code: https://github.com/joaopauloschuler/less-parameters-llm
Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance.
The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena.
🔑 Key Findings:
• 77% parameter reduction.
• Maintained model capabilities.
• Improved generalization.
Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT
Code: https://github.com/joaopauloschuler/less-parameters-llm
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LM Studio produces gibberish (GGUF)
4
#1 opened almost 2 years ago
by jrell
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model almost 2 years ago
llama.cpp error: 'done_getting_tensors: wrong number of tensors; expected 292, got 291'
2
#1 opened about 2 years ago
by jrell
"llama.cpp error: 'error loading model vocabulary: unknown pre-tokenizer type: 'dolphin12b''"
👍 1
23
#1 opened about 2 years ago
by jrell
reacted to singhsidhukuldeep's post with ❤️ over 2 years ago
Post
1462
You are all happy 😊 that @meta-llama released Llama 3.
Then you are sad 😔 that it only has a context length of 8k.
Then you are happy 😄 that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.
But then you are sad 😢 it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).
But then you are happy 😁 that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).
Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" 📜.
The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuning⚙️.
The training cycle is highly efficient, taking "only" 😂 8 hours on a single 8xA800 (80G) GPU machine.
The model also preserves its original capability over short contexts. ✁
The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.📊
The paper suggests that the context length could be extended far beyond 80K with more computation resources (😅 GPU-poor).
The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the ❤️ community.
Paper: https://arxiv.org/abs/2404.19553
This is where we are... until next time... 🌟
Extending Llama-3's Context Ten-Fold Overnight (2404.19553)
Then you are sad 😔 that it only has a context length of 8k.
Then you are happy 😄 that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.
But then you are sad 😢 it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).
But then you are happy 😁 that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).
Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" 📜.
The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuning⚙️.
The training cycle is highly efficient, taking "only" 😂 8 hours on a single 8xA800 (80G) GPU machine.
The model also preserves its original capability over short contexts. ✁
The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.📊
The paper suggests that the context length could be extended far beyond 80K with more computation resources (😅 GPU-poor).
The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the ❤️ community.
Paper: https://arxiv.org/abs/2404.19553
This is where we are... until next time... 🌟
Extending Llama-3's Context Ten-Fold Overnight (2404.19553)
LM Studio crash
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9
#2 opened over 2 years ago
by jrell
liked 2
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How to run this Model ?
8
#1 opened about 3 years ago
by deleted