Instructions to use yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B with Transformers:

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

pipe = pipeline("text-generation", model="yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B")
model = AutoModelForMultimodalLM.from_pretrained("yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B")
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 yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B

SGLang

How to use yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B 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 "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B" \
    --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": "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B",
		"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 "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B" \
        --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": "yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B with Docker Model Runner:
```
docker model run hf.co/yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B
```

aligned_ft_data_M2_FFT_Qwen3_14B_20260310_212832_presence0.0_freq0.0_repetition1.0_loop5-200_rep3_cutoff16384_20260326_150447

This model is a fine-tuned version of Qwen/Qwen3-14B on the aligned_ft_data_M2_FFT_Qwen3_14B_20260310_212832_presence0.0_freq0.0_repetition1.0_loop5-200_rep3 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: 1e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1.0

Training results

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.22.0

Downloads last month: -

Safetensors

Model size

425k params

Tensor type

BF16

Model tree for yileitu/Mdist_14B_Chem_690K_fulldata_from_Mspec_FFT_Qwen3_14B

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Finetuned

(271)

this model