mlx-community/Qwen2.5-7B-Instruct-Uncensored-4bit
The Model mlx-community/Qwen2.5-7B-Instruct-Uncensored-4bit was converted to MLX format from Orion-zhen/Qwen2.5-7B-Instruct-Uncensored using mlx-lm version 0.19.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-7B-Instruct-Uncensored-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 mlx-community/Qwen2.5-7B-Instruct-Uncensored-4bit
Base model
Qwen/Qwen2.5-7B
Finetuned
Qwen/Qwen2.5-7B-Instruct
Datasets used to train mlx-community/Qwen2.5-7B-Instruct-Uncensored-4bit
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard72.040
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard35.830
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.360
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.050
- acc_norm on MuSR (0-shot)Open LLM Leaderboard13.580
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard38.070