Instructions to use sam2ai/qwen3-32b-en-indic-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sam2ai/qwen3-32b-en-indic-mt with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B")
model = PeftModel.from_pretrained(base_model, "sam2ai/qwen3-32b-en-indic-mt")

Transformers

How to use sam2ai/qwen3-32b-en-indic-mt with Transformers:

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

pipe = pipeline("text-generation", model="sam2ai/qwen3-32b-en-indic-mt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sam2ai/qwen3-32b-en-indic-mt")
model = AutoModelForCausalLM.from_pretrained("sam2ai/qwen3-32b-en-indic-mt", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sam2ai/qwen3-32b-en-indic-mt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sam2ai/qwen3-32b-en-indic-mt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sam2ai/qwen3-32b-en-indic-mt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sam2ai/qwen3-32b-en-indic-mt

SGLang

How to use sam2ai/qwen3-32b-en-indic-mt 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 "sam2ai/qwen3-32b-en-indic-mt" \
    --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": "sam2ai/qwen3-32b-en-indic-mt",
		"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 "sam2ai/qwen3-32b-en-indic-mt" \
        --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": "sam2ai/qwen3-32b-en-indic-mt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sam2ai/qwen3-32b-en-indic-mt with Docker Model Runner:
```
docker model run hf.co/sam2ai/qwen3-32b-en-indic-mt
```

See axolotl config

axolotl version: 0.12.2

base_model: Qwen/Qwen3-32B
# Automatically upload checkpoint and final model to HF
hub_model_id: sam2ai/qwen3-32b-en-indic-mt

  #plugins:
  #- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

chat_template: qwen3
datasets:
  - path: sam2ai/en-oriya-translation
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles:
      assistant:
        - gpt
      user:
        - human

val_set_size: 0.0
output_dir: ./outputs/Qwen3/Qwen3-32b-wat25
dataset_prepared_path: last_run_prepared

sequence_len: 1096
sample_packing: true
eval_sample_packing: true


load_in_4bit: true
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - down_proj
  - up_proj
lora_mlp_kernel: true
lora_qkv_kernel: true
lora_o_kernel: true

wandb_project: QWEN3-WAT2025
wandb_entity:
wandb_watch:
wandb_name: Qwen3-27B-en-indic-mt
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_torch_4bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: false

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:

# save_first_step: true  # unc
#
#
# omment this to validate checkpoint saving works with your config

qwen3-32b-en-indic-mt

This model is a fine-tuned version of Qwen/Qwen3-32B on the sam2ai/en-oriya-translation 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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_4BIT 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: 274
training_steps: 2745

Training results

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.7.0+gitf717b2a
Datasets 4.0.0
Tokenizers 0.21.4

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qwen3-32b-en-indic-mt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for sam2ai/qwen3-32b-en-indic-mt

Dataset used to train sam2ai/qwen3-32b-en-indic-mt