metadata
license: apache-2.0
datasets:
- Nikity/Kyoto-Corpus
language:
- en
base_model: mlx-community/lille-130m-instruct-fp16
base_model_relation: finetune
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
model-index:
- name: lille-130m-instruct
results:
- task:
type: text-generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- type: Accuracy
value: 15.05
name: ARC (Challenge)
- task:
type: text-generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- type: Accuracy
value: 21.4
name: ARC (Easy)
- task:
type: text-generation
dataset:
name: gpqa
type: gpqa
metrics:
- type: Accuracy
value: 12.73
name: GPQA
- task:
type: text-generation
dataset:
name: gsm8k
type: gsm8k
metrics:
- type: Accuracy
value: 7.73
name: GSM8K
- task:
type: text-generation
dataset:
name: ifeval
type: ifeval
metrics:
- type: Accuracy
value: 9.01
name: IFEVAL
- task:
type: text-generation
dataset:
name: math
type: math
metrics:
- type: Accuracy
value: 1.91
name: MATH (Level 5)
- task:
type: text-generation
dataset:
name: mmlu
type: mmlu
metrics:
- type: Accuracy
value: 22.76
name: MMLU
- task:
type: text-generation
dataset:
name: mt_bench
type: mt_bench
metrics:
- type: Accuracy
value: 8.2
name: MT-Bench
- task:
type: text-generation
dataset:
name: truthful_qa
type: truthful_qa
metrics:
- type: Accuracy
value: 9.06
name: TruthfulQA
mlx-community/lille-130m-instruct-8bit
This model mlx-community/lille-130m-instruct-8bit was converted to MLX format from mlx-community/lille-130m-instruct-fp16 using mlx-lm version 0.27.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/lille-130m-instruct-8bit")
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)