Instructions to use sid172002/deepseek-math-7b-3epoch-678k-fullft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sid172002/deepseek-math-7b-3epoch-678k-fullft with Transformers:

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

pipe = pipeline("text-generation", model="sid172002/deepseek-math-7b-3epoch-678k-fullft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sid172002/deepseek-math-7b-3epoch-678k-fullft")
model = AutoModelForCausalLM.from_pretrained("sid172002/deepseek-math-7b-3epoch-678k-fullft")
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 sid172002/deepseek-math-7b-3epoch-678k-fullft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sid172002/deepseek-math-7b-3epoch-678k-fullft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sid172002/deepseek-math-7b-3epoch-678k-fullft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sid172002/deepseek-math-7b-3epoch-678k-fullft

SGLang

How to use sid172002/deepseek-math-7b-3epoch-678k-fullft 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 "sid172002/deepseek-math-7b-3epoch-678k-fullft" \
    --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": "sid172002/deepseek-math-7b-3epoch-678k-fullft",
		"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 "sid172002/deepseek-math-7b-3epoch-678k-fullft" \
        --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": "sid172002/deepseek-math-7b-3epoch-678k-fullft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sid172002/deepseek-math-7b-3epoch-678k-fullft with Docker Model Runner:
```
docker model run hf.co/sid172002/deepseek-math-7b-3epoch-678k-fullft
```

🚀 DeepSeek Math 7B - Full Fine-tune

Model ID: sid172002/deepseek-math-7b-3epoch-678k-fullft

Fully fine-tuned DeepSeek Math 7B on 678K high-quality math problems. +17.8% improvement over base model on GSM8K (64.2% → 82.0%).

📊 Quick Stats

Metric	Value
Parameters	7 Billion
Training Steps	63,609
Epochs	3.0
Dataset Size	678,494 samples
GSM8K Score	82.0%
Training Loss	0.6394
Eval Loss	0.6411

🏆 Benchmarks

Benchmark	Score	Base	Improvement
GSM8K	82.0%	64.2%	+17.8%
MathBench	92.0%	~70%	+22%
MMLU	Pending	-	Leaderboard Eval
MATH	TBD	33.2%	In Progress

💻 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "sid172002/deepseek-math-7b-3epoch-678k-fullft",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "sid172002/deepseek-math-7b-3epoch-678k-fullft",
    trust_remote_code=True
)

# Solve math problem
problem = "What is 2x + 5 = 13?"
prompt = f"Solve step by step:\n\n{problem}\n\nSolution:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for sid172002/deepseek-math-7b-3epoch-678k-fullft

Base model

deepseek-ai/deepseek-math-7b-base

Finetuned

(25)

this model

Datasets used to train sid172002/deepseek-math-7b-3epoch-678k-fullft

Evaluation results

accuracy on GSM8K
self-reported

82.000