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Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token
noneUsername/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token
Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token is a machine learning model from noneUsername. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Note: This model is no longer the optimal W8A8 quantization, please consider using a better quantization model I made later: noneUsername/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token-better
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From the Hugging Face model README
Note: This model is no longer the optimal W8A8 quantization, please consider using a better quantization model I made later: noneUsername/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token-better
My first quantization uses the quantization method provided by vllm:
https://docs.vllm.ai/en/latest/quantization/int8.html
NUM_CALIBRATION_SAMPLES = 2048
MAX_SEQUENCE_LENGTH = 8192
smoothing_strength=0.8
I will verify the validity of the model and update the readme as soon as possible.
edit: The performance in my ERP test was comparable to Mistral-Nemo-Instruct-2407-GPTQ-INT8, which I consider a successful quantization.
vllm (pretrained=/root/autodl-tmp/Mistral-Nemo-Instruct-2407,add_bos_token=true,tensor_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.85,swap_space=0), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| gsm8k | 3 | flexible-extract | 5 | exact_match | ↑ | 0.800 | ± | 0.0253 |
| strict-match | 5 | exact_match | ↑ | 0.784 | ± | 0.0261 |
lm_eval --model vllm --model_args pretrained="/mnt/e/Code/models/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token",add_bos_token=true,dtype=half,tensor_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.85,swap_space=0 --tasks gsm8k --num_fewshot 5 --limit 250 --batch_size 1 vllm (pretrained=/mnt/e/Code/models/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token,add_bos_token=true,dtype=half,tensor_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.85,swap_space=0), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: 1
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| gsm8k | 3 | flexible-extract | 5 | exact_match | ↑ | 0.784 | ± | 0.0261 |
| strict-match | 5 | exact_match | ↑ | 0.768 | ± | 0.0268 | ||
| In gsm8k, still a bit worse than the original... |
lm_eval --model vllm
l_args > --model_args pretrained="/mnt/e/Code/models/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token",add_bos_token=true,dtype=half,tensor_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.85,swap_space=0
ks hellaswag \
--tasks hellaswag
--limit 150
--num_fewshot 10
--batch_size 1
vllm (pretrained=/mnt/e/Code/models/Mistral-Nemo-Instruct-2407-W8A8-Dynamic-Per-Token,add_bos_token=true,dtype=half,tensor_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.85,swap_space=0), gen_kwargs: (None), limit: 150.0, num_fewshot: 10, batch_size: 1
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| hellaswag | 1 | none | 10 | acc | ↑ | 0.5800 | ± | 0.0404 |
| none | 10 | acc_norm | ↑ | 0.7533 | ± | 0.0353 |
- Type
- mistral
- Created
- Oct 4, 2024
- Updated
- Oct 19, 2024