Instructions to use cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo with Transformers:

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

pipe = pipeline("text-generation", model="cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo")
model = AutoModelForCausalLM.from_pretrained("cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo")
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 cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo

SGLang

How to use cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo 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 "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo" \
    --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": "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo",
		"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 "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo" \
        --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": "cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo with Docker Model Runner:
```
docker model run hf.co/cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: meta-llama/Llama-3.2-3B-Instruct
trust_remote_code: true
strict: false

chat_template: llama3

load_in_8bit: false
load_in_4bit: false

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: ./outputs/dataset_tokenlab/train
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/train
    type: chat_template
    weight: 0.2

validation_datasets:
  - path: ./outputs/dataset_tokenlab/validation
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/validation
    type: chat_template
    weight: 0.2

test_datasets:
  - path: ./outputs/dataset_tokenlab/test
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/test
    type: chat_template
    weight: 0.2

val_set_size: 0.0

dataset_prepared_path: ./outputs/dataset_prepared
output_dir: ./outputs/cemig-sft-2gpu/

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
flash_attn: true

gradient_checkpointing: true
micro_batch_size: 4
gradient_accumulation_steps: 4

num_epochs: 2
optimizer: adamw_torch_fused
learning_rate: 1.0e-5
lr_scheduler: cosine
cosine_constant_lr_ratio: 0
cosine_min_lr_ratio: 0.1
warmup_ratio: 0.1
weight_decay: 0.0

bf16: true
tf32: true
save_only_model: true

logging_steps: 1
evals_per_epoch: 4
saves_per_epoch: 2

special_tokens:
  pad_token: <|finetune_right_pad_id|>

dataloader_num_workers: 4
dataloader_prefetch_factor: 2

wandb_project: llama32-3b-dados-cemig
wandb_entity: null
wandb_name: llama3.2-3b-tokenlab-more-cemig-data
wandb_log_model: checkpoint

outputs/cemig-sft-2gpu/

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7199
Memory/max Active (gib): 47.38
Memory/max Allocated (gib): 47.38
Memory/device Reserved (gib): 47.9

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3136
training_steps: 31360

Training results

Training Loss	Epoch	Step	Validation Loss	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	2.6370	33.94	33.94	34.49
0.7927	0.2500	3920	0.8321	47.38	47.38	49.42
0.7541	0.5000	7840	0.7620	47.38	47.38	47.9
0.7028	0.7500	11760	0.7397	47.38	47.38	47.9
0.6949	1.0	15680	0.7298	47.38	47.38	47.9
0.6793	1.2500	19600	0.7247	47.38	47.38	47.9
0.71	1.5000	23520	0.7221	47.38	47.38	47.9
0.6818	1.7500	27440	0.7207	47.38	47.38	47.9
0.6828	2.0	31360	0.7199	47.38	47.38	47.9

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu130
Datasets 4.3.0
Tokenizers 0.22.1

Downloads last month: 1

Safetensors

Model size

3B params

Tensor type

F32

BF16

Model tree for cemig-temp/llama3.2-3b-instruct-tokenlab-cemigConvo

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

(1606)

this model