Fmuaddib/Configurable-Yi-1.5-9B-Chat-mlx-8Bit
The Model Fmuaddib/Configurable-Yi-1.5-9B-Chat-mlx-8Bit was converted to MLX format from vicgalle/Configurable-Yi-1.5-9B-Chat using mlx-lm version 0.22.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Fmuaddib/Configurable-Yi-1.5-9B-Chat-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Base model
vicgalle/Configurable-Yi-1.5-9B-ChatDataset used to train Fmuaddib/Configurable-Yi-1.5-9B-Chat-mlx-8Bit
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard64.160
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.700
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard70.990
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard58.750
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard76.800
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.580
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard43.230
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard35.330
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard6.120
- acc_norm on GPQA (0-shot)Open LLM Leaderboard12.420