fengpeisheng1/thea-rp-3b-25r-mlx-4Bit
The Model fengpeisheng1/thea-rp-3b-25r-mlx-4Bit was converted to MLX format from lunahr/thea-rp-3b-25r using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("fengpeisheng1/thea-rp-3b-25r-mlx-4Bit")
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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Model tree for fengpeisheng1/thea-rp-3b-25r-mlx-4Bit
Base model
meta-llama/Llama-3.2-3B-Instruct
Finetuned
SicariusSicariiStuff/Impish_LLAMA_3B
Finetuned
lunahr/thea-rp-3b-25r
Datasets used to train fengpeisheng1/thea-rp-3b-25r-mlx-4Bit
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard65.780
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard20.010
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard11.710
- acc_norm on GPQA (0-shot)Open LLM Leaderboard3.240
- acc_norm on MuSR (0-shot)Open LLM Leaderboard5.930
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard22.890