Instructions to use PygTesting/pyg3v1-nemo-3ep-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PygTesting/pyg3v1-nemo-3ep-ckpts with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PygTesting/pyg3v1-nemo-3ep-ckpts")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PygTesting/pyg3v1-nemo-3ep-ckpts")
model = AutoModelForCausalLM.from_pretrained("PygTesting/pyg3v1-nemo-3ep-ckpts")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PygTesting/pyg3v1-nemo-3ep-ckpts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PygTesting/pyg3v1-nemo-3ep-ckpts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygTesting/pyg3v1-nemo-3ep-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PygTesting/pyg3v1-nemo-3ep-ckpts

SGLang

How to use PygTesting/pyg3v1-nemo-3ep-ckpts with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PygTesting/pyg3v1-nemo-3ep-ckpts" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygTesting/pyg3v1-nemo-3ep-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PygTesting/pyg3v1-nemo-3ep-ckpts" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PygTesting/pyg3v1-nemo-3ep-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PygTesting/pyg3v1-nemo-3ep-ckpts with Docker Model Runner:
```
docker model run hf.co/PygTesting/pyg3v1-nemo-3ep-ckpts
```

See axolotl config

axolotl version: 0.4.1

base_model: mistralai/Mistral-Nemo-Base-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

chat_template: chatml

datasets:
  - path: PygTesting/pyg3v1
    type: sharegpt
    conversation: chatml

hub_model_id: PygTesting/pyg3v1-nemo-3ep-ckpts
hub_strategy: every_save
hf_use_auth_token: true

dataset_prepared_path: ./data/pyg3v1-data/tokenized
val_set_size: 0.0
output_dir: ./data/pyg3v1-nemo-2eps-out

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

wandb_project: pyg3v1-nemo
wandb_entity:
wandb_watch:
wandb_name: more_eps_lower_lr
wandb_log_model:
 
 #unsloth_cross_entropy_loss: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0000075

train_on_inputs: false
group_by_length: false
bf16: auto
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: 0.03
evals_per_epoch: 0
eval_table_size:
saves_per_epoch: 3
debug:
deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
  pad_token: <pad>

pyg3v1-nemo-3ep-ckpts

This model is a fine-tuned version of mistralai/Mistral-Nemo-Base-2407 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.5e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 29
num_epochs: 3

Training results

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.0+rocm6.1
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for PygTesting/pyg3v1-nemo-3ep-ckpts

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

(85)

this model