Instructions to use AlexHung29629/sllama-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/sllama-6 with Transformers:

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

pipe = pipeline("text-generation", model="AlexHung29629/sllama-6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexHung29629/sllama-6")
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/sllama-6")
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/sllama-6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/sllama-6"
# 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/sllama-6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/sllama-6

SGLang

How to use AlexHung29629/sllama-6 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/sllama-6" \
    --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/sllama-6",
		"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/sllama-6" \
        --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/sllama-6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/sllama-6 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/sllama-6
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: /home/alex/Workspace/sllama/out_5/checkpoint-1722000
trust_remote_code: true
resize_token_embeddings_to_32x: true
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true

unfrozen_parameters:
  - ^(?![\s\S]*embed_tokens)[\s\S]+$

datasets:
  - path: lima.jsonl
    type: chat_template

dataloader_num_workers: 0
group_by_length: false
dataset_prepared_path: data_prep
output_dir: ./out_6_lima
dataloader_pin_memory: true
shuffle_merged_datasets: true

sequence_len: 2048
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

use_tensorboard: true
use_wandb: true
wandb_project: sllama

gradient_accumulation_steps: 1

micro_batch_size: 1
num_epochs: 4
#max_steps: 100000
save_steps: 100
save_total_limit: 2
save_only_model: true
optimizer: sgd
optim_args:
  momentum: 0.98
lr_scheduler: cosine
learning_rate: 0.1
#embedding_lr: 5e-7
cosine_constant_lr_ratio: 0.1
max_grad_norm: 1.0

bf16: auto
fp8: true

gradient_checkpointing: false
gradient_checkpointing_kwargs:
  use_reentrant: false

logging_steps: 10
torch_compile: true
torch_compile_backend: inductor
torch_compile_mode: default
flash_attention: true

warmup_ratio: 0.05
weight_decay: 0.01

out_6_lima

This model was trained from scratch on the lima.jsonl 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: 0.1
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.SGD and the args are: momentum=0.98
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 64
training_steps: 1288

Training results

Framework versions

Transformers 4.56.1
Pytorch 2.7.1+cu128
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 5

Safetensors

Model size

2B params

Tensor type

BF16