A newer version of this model is available:
allenai/Llama-3.1-Tulu-3.1-8B
Scotto2025/Llama-3.1-Tulu-3-8B-Q6-mlx
The Model Scotto2025/Llama-3.1-Tulu-3-8B-Q6-mlx was converted to MLX format from allenai/Llama-3.1-Tulu-3-8B using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Scotto2025/Llama-3.1-Tulu-3-8B-Q6-mlx")
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 Scotto2025/Llama-3.1-Tulu-3-8B-Q6-mlx
Base model
meta-llama/Llama-3.1-8B
Finetuned
allenai/Llama-3.1-Tulu-3-8B-SFT
Finetuned
allenai/Llama-3.1-Tulu-3-8B-DPO
Finetuned
allenai/Llama-3.1-Tulu-3-8B
Dataset used to train Scotto2025/Llama-3.1-Tulu-3-8B-Q6-mlx
Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard82.550
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard16.860
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard18.880
- acc_norm on GPQA (0-shot)Open LLM Leaderboard6.260
- acc_norm on MuSR (0-shot)Open LLM Leaderboard10.520
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard20.230