wizardeur/Rombos-LLM-V2.6-Qwen-14b-mlx-4bit
The Model wizardeur/Rombos-LLM-V2.6-Qwen-14b-mlx-4bit was converted to MLX format from rombodawg/Rombos-LLM-V2.6-Qwen-14b using mlx-lm version 0.19.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("wizardeur/Rombos-LLM-V2.6-Qwen-14b-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 wizardeur/Rombos-LLM-V2.6-Qwen-14b-mlx-4bit
Base model
Qwen/Qwen2.5-14B
Finetuned
Qwen/Qwen2.5-14B-Instruct
Finetuned
rombodawg/Rombos-LLM-V2.6-Qwen-14b
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard52.140
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard49.220
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard28.850
- acc_norm on GPQA (0-shot)Open LLM Leaderboard17.000
- acc_norm on MuSR (0-shot)Open LLM Leaderboard19.260
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard48.850