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RichardErkhov/
Magpie-Align_-_MagpieLM-4B-Chat-v0.1-gguf

Name: Magpie-Align_-_MagpieLM-4B-Chat-v0.1-gguf
Availability: InStock

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MagpieLM-4B-Chat-v0.1 - GGUF

Model creator: https://huggingface.co/Magpie-Align/
Original model: https://huggingface.co/Magpie-Align/MagpieLM-4B-Chat-v0.1/

Name	Quant method	Size
MagpieLM-4B-Chat-v0.1.Q2_K.gguf	Q2_K	1.71GB
MagpieLM-4B-Chat-v0.1.IQ3_XS.gguf	IQ3_XS	1.89GB
MagpieLM-4B-Chat-v0.1.IQ3_S.gguf	IQ3_S	1.97GB
MagpieLM-4B-Chat-v0.1.Q3_K_S.gguf	Q3_K_S	1.96GB
MagpieLM-4B-Chat-v0.1.IQ3_M.gguf	IQ3_M	2.03GB
MagpieLM-4B-Chat-v0.1.Q3_K.gguf	Q3_K	2.14GB
MagpieLM-4B-Chat-v0.1.Q3_K_M.gguf	Q3_K_M	2.14GB
MagpieLM-4B-Chat-v0.1.Q3_K_L.gguf	Q3_K_L	2.3GB
MagpieLM-4B-Chat-v0.1.IQ4_XS.gguf	IQ4_XS	2.38GB
MagpieLM-4B-Chat-v0.1.Q4_0.gguf	Q4_0	2.47GB
MagpieLM-4B-Chat-v0.1.IQ4_NL.gguf	IQ4_NL	2.49GB
MagpieLM-4B-Chat-v0.1.Q4_K_S.gguf	Q4_K_S	2.48GB
MagpieLM-4B-Chat-v0.1.Q4_K.gguf	Q4_K	2.59GB
MagpieLM-4B-Chat-v0.1.Q4_K_M.gguf	Q4_K_M	2.59GB
MagpieLM-4B-Chat-v0.1.Q4_1.gguf	Q4_1	2.71GB
MagpieLM-4B-Chat-v0.1.Q5_0.gguf	Q5_0	2.95GB
MagpieLM-4B-Chat-v0.1.Q5_K_S.gguf	Q5_K_S	2.95GB
MagpieLM-4B-Chat-v0.1.Q5_K.gguf	Q5_K	3.01GB
MagpieLM-4B-Chat-v0.1.Q5_K_M.gguf	Q5_K_M	3.01GB
MagpieLM-4B-Chat-v0.1.Q5_1.gguf	Q5_1	3.19GB
MagpieLM-4B-Chat-v0.1.Q6_K.gguf	Q6_K	3.46GB
MagpieLM-4B-Chat-v0.1.Q8_0.gguf	Q8_0	4.47GB

Original model description:

library_name: transformers license: other base_model: Magpie-Align/MagpieLM-4B-SFT-v0.1 tags:

alignment-handbook
trl
dpo
generated_from_trainer datasets:
Magpie-Align/MagpieLM-SFT-Data-v0.1
Magpie-Align/MagpieLM-DPO-Data-v0.1 model-index:
name: MagpieLM-4B-Chat-v0.1 results: []

🐦 MagpieLM-4B-Chat-v0.1

Visualize in Weights & Biases

🧐 About This Model

Model full name: Llama3.1-MagpieLM-4B-Chat-v0.1

This model is an aligned version of Llama-3.1-Minitron-4B-Width, which achieves state-of-the-art performance among open-aligned SLMs. It even outperforms larger open-weight models including Llama-3-8B-Instruct, Llama-3.1-8B-Instruct and Qwen-2-7B-Instruct.

We apply the following standard alignment pipeline with two carefully crafted synthetic datasets. Feel free to use these datasets and reproduce our model, or make your own friendly chatbots :)

We first perform SFT using Magpie-Align/MagpieLM-SFT-Data-v0.1.

SFT Model Checkpoint: Magpie-Align/MagpieLM-4B-SFT-v0.1

We then perform DPO on the Magpie-Align/MagpieLM-DPO-Data-v0.1 dataset.

See more powerful 8B version here!

🔥 Benchmark Performance

Greedy Decoding

Alpaca Eval 2: 40.99 (LC), 45.19 (WR)
Arena Hard: 24.6
WildBench WB Score (v2.0625): 32.37

Benchmark Performance Compare to Other SOTA SLMs

👀 Other Information

License: Please follow NVIDIA Open Model License Agreement.

Conversation Template: Please use the Llama 3 chat template for the best performance.

Limitations: This model primarily understands and generates content in English. Its outputs may contain factual errors, logical inconsistencies, or reflect biases present in the training data. While the model aims to improve instruction-following and helpfulness, it isn't specifically designed for complex reasoning tasks, potentially leading to suboptimal performance in these areas. Additionally, the model may produce unsafe or inappropriate content, as no specific safety training were implemented during the alignment process.

🧐 How to use it?

Please update transformers to the latest version by pip install git+https://github.com/huggingface/transformers.

You can then run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

python

import transformers
import torch

model_id = "MagpieLM-4B-Chat-v0.1"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Magpie, a friendly AI assistant."},
    {"role": "user", "content": "Who are you?"},
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Alignment Pipeline

The detailed alignment pipeline is as follows.

Stage 1: Supervised Fine-tuning

We use Axolotl for SFT. Please refer to the model card of SFT checkpoint and below for detailed configurations.

See axolotl config

axolotl version: 0.4.1

yaml

base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: llama3

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Magpie-Align/MagpieLM-SFT-Data-v0.1
    type: sharegpt
    conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/MagpieLM-4B-SFT-v0.1

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama3.1-MagpieLM-4B-SFT-v0.1
wandb_log_model:
hub_model_id: Magpie-Align/MagpieLM-4B-SFT-v0.1

gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

Stage 2: Direct Preference Optimization

We use alignment handbook for DPO.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1.5e-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6911	0.0653	100	0.6912	-0.0026	-0.0066	0.5640	0.0041	-502.9037	-510.6042	-1.7834	-1.7781
0.6703	0.1306	200	0.6713	-0.1429	-0.1981	0.6380	0.0552	-522.0521	-524.6394	-1.7686	-1.7593
0.6306	0.1959	300	0.6347	-0.6439	-0.8210	0.6840	0.1770	-584.3356	-574.7375	-1.7536	-1.7436
0.5831	0.2612	400	0.5932	-1.5155	-1.8774	0.7070	0.3619	-689.9788	-661.8920	-1.6963	-1.6877
0.5447	0.3266	500	0.5645	-2.1858	-2.7052	0.7110	0.5195	-772.7636	-728.9221	-1.6249	-1.6207
0.5896	0.3919	600	0.5453	-2.3771	-2.9747	0.7180	0.5976	-799.7122	-748.0584	-1.5836	-1.5847
0.5342	0.4572	700	0.5305	-2.6231	-3.3063	0.7350	0.6832	-832.8744	-772.6592	-1.5454	-1.5524
0.511	0.5225	800	0.5177	-3.0517	-3.8393	0.7400	0.7876	-886.1714	-815.5145	-1.5160	-1.5273
0.5007	0.5878	900	0.5088	-3.0925	-3.9197	0.7540	0.8273	-894.2120	-819.5908	-1.5007	-1.5144
0.485	0.6531	1000	0.5033	-3.1305	-3.9863	0.7630	0.8558	-900.8680	-823.3940	-1.4834	-1.4997
0.4307	0.7184	1100	0.4989	-3.1387	-4.0097	0.7610	0.8710	-903.2113	-824.2159	-1.4728	-1.4911
0.5403	0.7837	1200	0.4964	-3.3418	-4.2574	0.7620	0.9156	-927.9747	-844.5242	-1.4641	-1.4822
0.5182	0.8490	1300	0.4952	-3.3255	-4.2430	0.7600	0.9175	-926.5396	-842.8945	-1.4601	-1.4788
0.5165	0.9144	1400	0.4943	-3.3308	-4.2525	0.7600	0.9217	-927.4913	-843.4282	-1.4610	-1.4799
0.5192	0.9797	1500	0.4942	-3.3377	-4.2603	0.7620	0.9226	-928.2655	-844.1144	-1.4591	-1.4783

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

See alignment handbook configs