metadata
license: mit
library_name: mlx
pipeline_tag: text-generation
datasets:
- yulan-team/YuLan-Mini-Datasets
- HuggingFaceFW/fineweb-edu
- bigcode/the-stack-v2
- mlfoundations/dclm-baseline-1.0
- math-ai/AutoMathText
- gair-prox/open-web-math-pro
- RUC-AIBOX/long_form_thought_data_5k
- internlm/Lean-Workbook
- internlm/Lean-Github
- deepseek-ai/DeepSeek-Prover-V1
- ScalableMath/Lean-STaR-base
- ScalableMath/Lean-STaR-plus
- ScalableMath/Lean-CoT-base
- ScalableMath/Lean-CoT-plus
- opencsg/chinese-fineweb-edu
- liwu/MNBVC
- vikp/textbook_quality_programming
- HuggingFaceTB/smollm-corpus
- OpenCoder-LLM/opc-annealing-corpus
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- XinyaoHu/AMPS_mathematica
- deepmind/math_dataset
- mrfakename/basic-math-10m
- microsoft/orca-math-word-problems-200k
- AI-MO/NuminaMath-CoT
- HuggingFaceTB/cosmopedia
- MU-NLPC/Calc-ape210k
- manu/project_gutenberg
- storytracer/LoC-PD-Books
- allenai/dolma
language:
- en
- zh
tags:
- code
- math
- mlx
arxiv: 2412.17743
base_model: yulan-team/YuLan-Mini
model-index:
- name: YuLan-Mini
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.64
name: pass@1
verified: false
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 0.659
name: pass@1
verified: false
- task:
type: text-generation
dataset:
name: MATH-500
type: math-500
metrics:
- type: maj@1
value: 0.378
name: maj@1
verified: false
- task:
type: text-generation
dataset:
name: GSM8K
type: gsm8k
metrics:
- type: maj@1
value: 0.684
name: maj@1
verified: false
IvanHU/YuLan-Mini-4bit
This model IvanHU/YuLan-Mini-4bit was converted to MLX format from yulan-team/YuLan-Mini using mlx-lm version 0.22.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("IvanHU/YuLan-Mini-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)