Instructions to use AlexHung29629/model_step1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/model_step1 with Transformers:

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

pipe = pipeline("text-generation", model="AlexHung29629/model_step1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexHung29629/model_step1")
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/model_step1")
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/model_step1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/model_step1"
# 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/model_step1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/model_step1

SGLang

How to use AlexHung29629/model_step1 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/model_step1" \
    --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/model_step1",
		"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/model_step1" \
        --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/model_step1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/model_step1 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/model_step1
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.8.0

base_model: AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text

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

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

datasets:
  - path: AlexHung29629/train_0415_input_output
    type: input_output
  - path: AlexHung29629/glaive-function-calling-v2-mistral
    type:
      system_prompt: ""
      field_system: system
      field_instruction: input
      field_output: output
      format: "{instruction}"
      no_input_format: "{instruction}"

dataset_prepared_path: ./sft_dataprep/
val_set_size: 0
output_dir: ./placeholder_embed/
shuffle_merged_datasets: false

sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

hub_model_id: AlexHung29629/model_step1

wandb_project: TP1_2025_05
wandb_entity:
wandb_watch:
wandb_name: Mistral-24B-SFT-250522_embed
wandb_log_model: checkpoint
use_tensorboard: true

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
max_steps: 1000
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 2e-5
max_grad_norm: 1.0

bf16: true
tf32: false

#gradient_checkpointing: false
#gradient_checkpointing_kwargs:
#  use_reentrant: false
logging_steps: 1
flash_attention: true
xformers_attention: false
sdp_attention: false

warmup_ratio: 0.01
saves_per_epoch:
save_steps: 1000
weight_decay: 0
#deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json

fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: true
  fsdp_cpu_ram_efficient_loading: true
  fsdp_activation_checkpointing: true
  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP


special_tokens:
  pad_token: "<pad>"
added_tokens_overrides:  # Dict[int, str]
  20: "<think>"
  21: "</think>"

seed: 42

model_step1

This model is a fine-tuned version of AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text 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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
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: 10
training_steps: 1000

Framework versions

Transformers 4.51.0
Pytorch 2.7.0+cu128
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: -

Safetensors

Model size

24B params

Tensor type

F32

BF16

Model tree for AlexHung29629/model_step1

Base model

AlexHung29629/Mistral-Small-3.1-24B-Instruct-2503-text

Finetuned

(1)

this model