Instructions to use qnguyen3/Quan-Mathy-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qnguyen3/Quan-Mathy-7B with Transformers:

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

pipe = pipeline("text-generation", model="qnguyen3/Quan-Mathy-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qnguyen3/Quan-Mathy-7B")
model = AutoModelForCausalLM.from_pretrained("qnguyen3/Quan-Mathy-7B")
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 qnguyen3/Quan-Mathy-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qnguyen3/Quan-Mathy-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qnguyen3/Quan-Mathy-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qnguyen3/Quan-Mathy-7B

SGLang

How to use qnguyen3/Quan-Mathy-7B 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 "qnguyen3/Quan-Mathy-7B" \
    --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": "qnguyen3/Quan-Mathy-7B",
		"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 "qnguyen3/Quan-Mathy-7B" \
        --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": "qnguyen3/Quan-Mathy-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qnguyen3/Quan-Mathy-7B with Docker Model Runner:
```
docker model run hf.co/qnguyen3/Quan-Mathy-7B
```

See axolotl config

axolotl version: 0.5.0

base_model: Qwen/Qwen2.5-Math-7B

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

strict: false

chat_template: chatml
datasets:

  - path: arcee-ai/orcamath_evol_85k
    type: chat_template
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value

  - path: allenai/tulu-3-sft-personas-math
    type: chat_template
    split: train[:10%]
    field_messages: messages
    message_field_role: role
    message_field_content: content

  - path: allenai/tulu-3-sft-personas-algebra
    type: chat_template
    split: train
    field_messages: messages
    message_field_role: role
    message_field_content: content

dataset_prepared_path: ./axolotl-datasets/math-evol-prepared
val_set_size: 0.02
output_dir: ./axolotl-outputs/Arcee-7B-Mathy-7B-6e

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: "Arcee-Mathy-7B"
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 6
optimizer: adamw_torch_fused #adamw_torch_fused # if you have OOM errors you can use adamw_8bit
lr_scheduler: linear
learning_rate: 5e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 50
evals_per_epoch: 1
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0

special_tokens:
  pad_token: <|endoftext|>
  eos_token: <|im_end|>

axolotl-outputs/Arcee-7B-Mathy-7B-6e

This model is a fine-tuned version of Qwen/Qwen2.5-Math-7B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.5608

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: 5e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 256
total_eval_batch_size: 32
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: linear
lr_scheduler_warmup_steps: 50
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss
0.4101	0.0106	1	1.6490
0.2319	0.9987	94	1.5007
0.2234	1.9960	188	1.5070
0.205	2.9920	282	1.5350
0.1979	3.9894	376	1.5456
0.1866	4.9867	470	1.5547
0.1926	5.9827	564	1.5608

Framework versions

Transformers 4.46.1
Pytorch 2.3.1+cu121
Datasets 3.0.1
Tokenizers 0.20.3

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for qnguyen3/Quan-Mathy-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Math-7B

Finetuned

(1027)

this model