Instructions to use apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B-Base")
model = PeftModel.from_pretrained(base_model, "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e")

Transformers

How to use apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e with Transformers:

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

pipe = pipeline("text-generation", model="apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e")
model = AutoModelForCausalLM.from_pretrained("apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e", 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 apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e

SGLang

How to use apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e 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 "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e" \
    --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": "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e",
		"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 "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e" \
        --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": "apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e with Docker Model Runner:
```
docker model run hf.co/apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e
```

See axolotl config

axolotl version: 0.12.0.dev0

adapter: lora
base_model: Qwen/Qwen3-8B-Base
bf16: true
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 45c346a7c1e52747_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: None
    field_instruction: instruct
    field_output: output
    field_system: None
    format: None
    no_input_format: None
    system_format: '{system}'
    system_prompt: None
ddp: true
debug: null
deepspeed: null
device_map: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: true
hub_model_id: apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 20
micro_batch_size: 40
mlflow_experiment_name: /tmp/45c346a7c1e52747_train_data.json
model_card: false
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
rl: null
s2_attention: null
sample_packing: true
save_steps: 100
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl: null
trust_remote_code: false
val_set_size: 0.0
wandb_name: c732d2b4-46df-4ed8-83ee-7525f648965f
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: c732d2b4-46df-4ed8-83ee-7525f648965f
warmup_steps: 200
weight_decay: 0
xformers_attention: null

095d4331-de34-4132-bce0-29cbd9134a0e

This model is a fine-tuned version of Qwen/Qwen3-8B-Base on an unknown 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: 40
eval_batch_size: 40
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB 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: 200
training_steps: 20

Training results

Framework versions

PEFT 0.16.0
Transformers 4.53.2
Pytorch 2.7.1+cu128
Datasets 4.0.0
Tokenizers 0.21.2

Downloads last month: 6

Model tree for apriasmoro/095d4331-de34-4132-bce0-29cbd9134a0e

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Qwen/Qwen3-8B-Base

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