55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
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---
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inference: false
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language:
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- en
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license: llama2
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model_type: llama
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pipeline_tag: text-generation
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tags:
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- facebook
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- meta
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- pytorch
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- llama
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- llama-2
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- h2ogpt
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---
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h2oGPT clone of [Meta's Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf).
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This model can be fine-tuned with [H2O.ai](https://h2o.ai/) open-source software:
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- h2oGPT https://github.com/h2oai/h2ogpt/
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- H2O LLM Studio https://h2o.ai/platform/ai-cloud/make/llm-studio/
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Try our live [h2oGPT demo](https://gpt.h2o.ai) with side-by-side LLM comparisons and private document chat!
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## Model Architecture
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```
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LlamaForCausalLM(
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(model): LlamaModel(
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(embed_tokens): Embedding(32000, 8192, padding_idx=0)
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(layers): ModuleList(
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(0-79): 80 x LlamaDecoderLayer(
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(self_attn): LlamaAttention(
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(q_proj): Linear4bit(in_features=8192, out_features=8192, bias=False)
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(k_proj): Linear4bit(in_features=8192, out_features=1024, bias=False)
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(v_proj): Linear4bit(in_features=8192, out_features=1024, bias=False)
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(o_proj): Linear4bit(in_features=8192, out_features=8192, bias=False)
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(rotary_emb): LlamaRotaryEmbedding()
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)
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(mlp): LlamaMLP(
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(gate_proj): Linear4bit(in_features=8192, out_features=28672, bias=False)
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(up_proj): Linear4bit(in_features=8192, out_features=28672, bias=False)
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(down_proj): Linear4bit(in_features=28672, out_features=8192, bias=False)
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(act_fn): SiLUActivation()
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)
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(input_layernorm): LlamaRMSNorm()
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(post_attention_layernorm): LlamaRMSNorm()
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)
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)
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(norm): LlamaRMSNorm()
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)
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(lm_head): Linear(in_features=8192, out_features=32000, bias=False)
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)
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```
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