Instructions to use Polygl0t/Tucano2-qwen-1.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Polygl0t/Tucano2-qwen-1.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Polygl0t/Tucano2-qwen-1.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Polygl0t/Tucano2-qwen-1.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Polygl0t/Tucano2-qwen-1.5B-Instruct", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Polygl0t/Tucano2-qwen-1.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Polygl0t/Tucano2-qwen-1.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Polygl0t/Tucano2-qwen-1.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Polygl0t/Tucano2-qwen-1.5B-Instruct

SGLang

How to use Polygl0t/Tucano2-qwen-1.5B-Instruct 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 "Polygl0t/Tucano2-qwen-1.5B-Instruct" \
    --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": "Polygl0t/Tucano2-qwen-1.5B-Instruct",
		"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 "Polygl0t/Tucano2-qwen-1.5B-Instruct" \
        --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": "Polygl0t/Tucano2-qwen-1.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Polygl0t/Tucano2-qwen-1.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/Polygl0t/Tucano2-qwen-1.5B-Instruct
```

Tucano2-qwen-1.5B-Instruct

File size: 2,728 Bytes

9d8593a

# Directory settings
checkpoint_dir: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct"
train_dataset_dir:
  # Total: 28,437 samples (x5 epochs)
  # Harmfull samples (without reasoning): 4,267 samples
  - /polyglot/portuguese/gigaverbo-v2-dpo/harmfull-no-reasoning
  # Harmfull samples (with reasoning, stripped): 4,008 samples
  - /polyglot/portuguese/gigaverbo-v2-dpo/harmfull-reasoning-stripped
  # Harmless samples (without reasoning): 10,521 samples
  - /polyglot/portuguese/gigaverbo-v2-dpo/harmless-no-reasoning
  # Harmless samples (with reasoning, stripped): 9,641 samples
  - /polyglot/portuguese/gigaverbo-v2-dpo/harmless-reasoning-stripped
val_dataset_dir: null
dataset_type: "jsonl"
cache_dir: "/lustre/mlnvme/data/polyglot/.cache"

# Data loading settings
pin_memory: true
num_workers_for_dataloader: 16
shuffle_dataset: true
mask_eos_token: false
mask_pad_token: false

# Model architecture settings
vocab_size: 49152
num_hidden_layers: 28
num_attention_heads: 16
num_key_value_heads: 8
head_dim: 128
hidden_size: 2048
intermediate_size: 6144
max_position_embeddings: 4096
tie_word_embeddings: true
hidden_act: "silu"
output_hidden_states: false
attn_implementation: "flash_attention_2"
use_cache: false
no_rope_layer_interval: null
rope_theta: 1000000.0
rope_scale_factor: null
rms_norm_eps: 0.000001

# Training settings
total_batch_size: 524288
micro_batch_size: 4
gradient_accumulation_steps: 4
eval_micro_batch_size: null
num_train_epochs: 5
warmup_ratio: 0.1
max_learning_rate: 0.000005
min_learning_rate: 0.0
muon_learning_rate: null
weight_decay: 0.0
beta1: 0.9
beta2: 0.95
eps: 0.00000001
lr_decay_type: "cosine"
use_sqrt: false
lr_decay_iters_coef: 1.
seed: 42
max_steps: 1115
max_grad_norm: 1.0

# APO settings
loss_type: "apo_zero"
dpo_beta: 0.5
precompute_ref_log_probs: true
truncation_mode: "keep_end"

# Precision and optimization settings
torch_compile: false
mat_mul_precision: "highest"
tf32: true
bf16: true
gradient_checkpointing: true
use_liger_kernel: false
static_graph: false

# Hub settings
push_to_hub: false
hub_token: null
hub_model_id: null

# Tokenizer and Reference model
tokenizer_name_or_path: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct-SFT"
chat_template_path: null
reference_model: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct-SFT"
continual_pretraining: true

# Checkpoint settings
resume_from_checkpoint: null
checkpointing_steps: 1000
begin_new_stage: true
stage_name: "single_cosine"

# Miscellaneous settings
sanity_check: false
sanity_check_num_samples: 100000
wandb_token: null
wandb_id: "tucano2-qwen-1.5b-instruct-apo"
wandb_project: "Polyglot"
wandb_desc: "Developing LLMs for low-resource languages"