Comments (3)
awni
commented on June 24, 2024
The probabilities that gets returned is the probability of the given token at that time step. To get the log probabilities you would just take the log of it mx.log(p).
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awni
commented on June 24, 2024
I would be happy to contribute a PR,
Thanks! It would be great to ease the path to evaluating models in MLX.
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chimezie
commented on June 24, 2024
Sounds good. I will contribute a PR for the server. Thanks for the input.
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Related Issues (20)
- Whisper stutters HOT 8
- mlx 0.13 very slow with q8 and fp16 HOT 5
- Fine tuned a Mixtral-8x7B-Instruct-v0.1 model and unable to load with AutoModelForCausalLM HOT 1
- Phi-3-mini-4k-instruct : Failing to stop at <|end|> on generating the answer. HOT 5
- PaliGemma 4bit Quantization broken and Inference issues. HOT 27
- [Feature Request] Function Calling for mlx_lm.server HOT 4
- OS system requirement for mlx HOT 1
- 01-ai/Yi-1.5-9B-Chat got ValueError: Cannot instantiate this tokenizer from a slow version. HOT 4
- CLIP Tokenizer unable to take text of unequal length (or token length) HOT 7
- Package 'mlx_whisper.assets' is absent from the `packages` configuration HOT 1
- Request for Example on Full Parameter and Training for LLM Model HOT 5
- Phi-3 128K Context Variants' `su` RoPE Scaling HOT 12
- [REGRESSION] Some MoE models display 0% GPU utilization with mlx-ops 0.14.0 HOT 3
- I would like to inquire about a solution to the following problem. HOT 1
- link to Phi-2 example in readme broken HOT 1
- SPMStreamingDetokenizer sometimes outputs incorrect multi-byte characters HOT 1
- Why change the module decomposition of whisper HOT 3
- A simple enhancement, in dataset creation time HOT 1
- [Question]about creating the 'adapters.npz' file HOT 3
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