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llava-v1.6-mistral-7b-hf

By llava-hf. 7.6 billion parameters, a context window of 32,768 tokens and the licence apache-2.0.

Facts

Released
2024-02-20 the day the repository was first published on Hugging Face Hugging Face, read
Licence
apache-2.0 the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
7.6 billion counted from the safetensors weight files Hugging Face, read
Active parameters
Unknown
Knowledge cutoff
Unknown
Context window
  • 32,768 tokens the maximum position embeddings in the model configuration model configuration, read
Longest output
Unknown
Tool calling
  • No the chat template in the tokenizer configuration has no place for tool definitions Hugging Face, read
Structured output
Unknown
Reasoning controls
Unknown
Inputs and outputs
text, image in, text out Hugging Face, read
Good for
  • Vision: Hugging Face files it under image-text-to-text Hugging Face, read

Prices

No source lists a price for this model. It may only run on your own hardware.

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
F16 estimate14.1 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q8_0 estimate7.5 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers
Q4_0 estimate4.0 GiBUnknown: no KV cache numbersUnknown: no KV cache numbers

Start it with a local runtime

  • vLLM
    vllm serve llava-hf/llava-v1.6-mistral-7b-hf
  • SGLang
    sglang serve --model-path llava-hf/llava-v1.6-mistral-7b-hf --port 30000

Use it from your harness

No endpoint in this directory serves this model yet.

Published results

These are results other people published. Baltor did not run them and does not rank models by them.