RichardErkhov/h2oai_-_h2ogpt-4096-llama2-7b-gguf

Quantization made by Richard Erkhov.

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h2ogpt-4096-llama2-7b - GGUF

• Model creator: https://huggingface.co/h2oai/
• Original model: https://huggingface.co/h2oai/h2ogpt-4096-llama2-7b/

Name	Quant method	Size
h2ogpt-4096-llama2-7b.Q2_K.gguf	Q2_K	2.36GB
h2ogpt-4096-llama2-7b.IQ3_XS.gguf	IQ3_XS	2.6GB
h2ogpt-4096-llama2-7b.IQ3_S.gguf	IQ3_S	2.75GB
h2ogpt-4096-llama2-7b.Q3_K_S.gguf	Q3_K_S	2.75GB
h2ogpt-4096-llama2-7b.IQ3_M.gguf	IQ3_M	2.9GB
h2ogpt-4096-llama2-7b.Q3_K.gguf	Q3_K	3.07GB
h2ogpt-4096-llama2-7b.Q3_K_M.gguf	Q3_K_M	3.07GB
h2ogpt-4096-llama2-7b.Q3_K_L.gguf	Q3_K_L	3.35GB
h2ogpt-4096-llama2-7b.IQ4_XS.gguf	IQ4_XS	3.4GB
h2ogpt-4096-llama2-7b.Q4_0.gguf	Q4_0	3.56GB
h2ogpt-4096-llama2-7b.IQ4_NL.gguf	IQ4_NL	3.58GB
h2ogpt-4096-llama2-7b.Q4_K_S.gguf	Q4_K_S	3.59GB
h2ogpt-4096-llama2-7b.Q4_K.gguf	Q4_K	3.8GB
h2ogpt-4096-llama2-7b.Q4_K_M.gguf	Q4_K_M	3.8GB
h2ogpt-4096-llama2-7b.Q4_1.gguf	Q4_1	3.95GB
h2ogpt-4096-llama2-7b.Q5_0.gguf	Q5_0	4.33GB
h2ogpt-4096-llama2-7b.Q5_K_S.gguf	Q5_K_S	4.33GB
h2ogpt-4096-llama2-7b.Q5_K.gguf	Q5_K	4.45GB
h2ogpt-4096-llama2-7b.Q5_K_M.gguf	Q5_K_M	4.45GB
h2ogpt-4096-llama2-7b.Q5_1.gguf	Q5_1	4.72GB
h2ogpt-4096-llama2-7b.Q6_K.gguf	Q6_K	5.15GB
h2ogpt-4096-llama2-7b.Q8_0.gguf	Q8_0	6.67GB

Original model description:

inference: false language: - en license: llama2 model_type: llama pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 - h2ogpt

h2oGPT clone of Meta's Llama 2 7B.

This model can be fine-tuned with H2O.ai open-source software:

• h2oGPT https://github.com/h2oai/h2ogpt/
• H2O LLM Studio https://h2o.ai/platform/ai-cloud/make/llm-studio/

Try our live h2oGPT demo with side-by-side LLM comparisons and private document chat!

Model Architecture

LlamaForCausalLM(
  (model): LlamaModel(
    (embed_tokens): Embedding(32000, 4096, padding_idx=0)
    (layers): ModuleList(
      (0-31): 32 x LlamaDecoderLayer(
        (self_attn): LlamaAttention(
          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)
          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)
          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
          (rotary_emb): LlamaRotaryEmbedding()
        )
        (mlp): LlamaMLP(
          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)
          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)
          (act_fn): SiLUActivation()
        )
        (input_layernorm): LlamaRMSNorm()
        (post_attention_layernorm): LlamaRMSNorm()
      )
    )
    (norm): LlamaRMSNorm()
  )
  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)