Instructions to use AlexHung29629/FormoMouse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/FormoMouse with Transformers:

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

pipe = pipeline("text-generation", model="AlexHung29629/FormoMouse")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexHung29629/FormoMouse")
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/FormoMouse")
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 AlexHung29629/FormoMouse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/FormoMouse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/FormoMouse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/FormoMouse

SGLang

How to use AlexHung29629/FormoMouse 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 "AlexHung29629/FormoMouse" \
    --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": "AlexHung29629/FormoMouse",
		"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 "AlexHung29629/FormoMouse" \
        --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": "AlexHung29629/FormoMouse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/FormoMouse with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/FormoMouse
```

See axolotl config

axolotl version: 0.9.2

base_model: AlexHung29629/FormoMouse123

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: false
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

unfrozen_parameters:
  - model.lm_head.weight
  - model.embed_tokens.weight

pretraining_dataset:
  - path: AlexHung29629/para_pat_text
    split: train
    text_column: text
    type: pretrain
  - path: AlexHung29629/urban_dictionary
    split: train
    text_column: text
    type: pretrain

dataset_prepared_path: data_prep_1
val_set_size: 0.0
output_dir: ./out_1

shuffle_merged_datasets: true

sequence_len: 2048
pretraining_sample_concatenation: true
#sample_packing: true
#eval_sample_packing: false
pad_to_sequence_len: true

use_tensorboard: true

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
max_steps: 10000
save_steps: 1000
save_total_limit: 1
save_only_model: true
optimizer: ao_adamw_fp8
lr_scheduler: cosine
learning_rate: 2e-4
max_grad_norm: 1.0

bfloat16: true

gradient_checkpointing: true

logging_steps: 1
torch_compile: false
sdp_attention: true


warmup_ratio: 0.1
weight_decay: 0.1

out_1

This model is a fine-tuned version of AlexHung29629/FormoMouse123 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: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 1000
training_steps: 10000

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu128
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 2

Safetensors

Model size

0.7B params

Tensor type

F32

BF16

Model tree for AlexHung29629/FormoMouse

Base model

AlexHung29629/FormoMouse123

Finetuned

(1)

this model