Instructions to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-Z1-9B-0414")
model = PeftModel.from_pretrained(base_model, "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles")

Transformers

How to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles with Transformers:

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

pipe = pipeline("text-generation", model="alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles

SGLang

How to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles 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 "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles" \
    --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": "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles",
		"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 "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles" \
        --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": "alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles with Docker Model Runner:
```
docker model run hf.co/alexiaassis/Modelo-Treinado-GLM-Z1-9B-0414-Ingles
```

Modelo-Treinado-GLM-Z1-9B-0414-Ingles / llamaboard_config.yaml

alexiaassis

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	top.booster: unsloth
	top.checkpoint_path: []
	top.finetuning_type: lora
	top.model_name: GLM-Z1-0414-9B-Chat
	top.quantization_bit: '4'
	top.quantization_method: bnb
	top.rope_scaling: none
	top.template: alpaca
	train.additional_target: ''
	train.apollo_rank: 16
	train.apollo_scale: 32
	train.apollo_target: all
	train.apollo_update_interval: 200
	train.badam_mode: layer
	train.badam_switch_interval: 50
	train.badam_switch_mode: ascending
	train.badam_update_ratio: 0.05
	train.batch_size: 4
	train.compute_type: bf16
	train.create_new_adapter: false
	train.cutoff_len: 8192
	train.dataset:
	- train_split2
	train.dataset_dir: data
	train.ds_offload: false
	train.ds_stage: none
	train.enable_thinking: true
	train.extra_args: '{"optim": "adamw_8bit", "weight_decay": 0.1, "label_smoothing_factor":
	0.0}'
	train.freeze_extra_modules: ''
	train.freeze_language_model: false
	train.freeze_multi_modal_projector: true
	train.freeze_trainable_layers: 2
	train.freeze_trainable_modules: all
	train.freeze_vision_tower: true
	train.galore_rank: 16
	train.galore_scale: 2
	train.galore_target: all
	train.galore_update_interval: 200
	train.gradient_accumulation_steps: 8
	train.image_max_pixels: 768*768
	train.image_min_pixels: 32*32
	train.learning_rate: 5e-5
	train.logging_steps: 10
	train.lora_alpha: 64
	train.lora_dropout: 0
	train.lora_rank: 32
	train.lora_target: ''
	train.loraplus_lr_ratio: 0
	train.lr_scheduler_type: cosine
	train.mask_history: false
	train.max_grad_norm: '1.0'
	train.max_samples: '3716'
	train.neat_packing: false
	train.neftune_alpha: 0
	train.num_train_epochs: '3.0'
	train.packing: false
	train.ppo_score_norm: false
	train.ppo_whiten_rewards: false
	train.pref_beta: 0.1
	train.pref_ftx: 0
	train.pref_loss: sigmoid
	train.report_to: none
	train.resize_vocab: false
	train.reward_model: []
	train.save_steps: 1000
	train.swanlab_api_key: ''
	train.swanlab_link: null
	train.swanlab_mode: cloud
	train.swanlab_project: llamafactory
	train.swanlab_run_name: ''
	train.swanlab_workspace: ''
	train.train_on_prompt: false
	train.training_stage: Supervised Fine-Tuning
	train.use_apollo: false
	train.use_badam: false
	train.use_dora: false
	train.use_galore: false
	train.use_llama_pro: false
	train.use_pissa: false
	train.use_rslora: true
	train.use_swanlab: false
	train.val_size: 0
	train.video_max_pixels: 256*256
	train.video_min_pixels: 16*16
	train.warmup_steps: 500