Instructions to use sngram/deepseek-7b-1280 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sngram/deepseek-7b-1280 with Transformers:

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

pipe = pipeline("text-generation", model="sngram/deepseek-7b-1280")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sngram/deepseek-7b-1280")
model = AutoModelForCausalLM.from_pretrained("sngram/deepseek-7b-1280", device_map="auto")
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 sngram/deepseek-7b-1280 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sngram/deepseek-7b-1280"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sngram/deepseek-7b-1280",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sngram/deepseek-7b-1280

SGLang

How to use sngram/deepseek-7b-1280 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 "sngram/deepseek-7b-1280" \
    --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": "sngram/deepseek-7b-1280",
		"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 "sngram/deepseek-7b-1280" \
        --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": "sngram/deepseek-7b-1280",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sngram/deepseek-7b-1280 with Docker Model Runner:
```
docker model run hf.co/sngram/deepseek-7b-1280
```

deepseek-7b-1280

Commit History

Upload tokenizer

f4d4b4a
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Sangram Patil commited on Feb 16, 2025

Upload Qwen2ForCausalLM

fc021bf
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Sangram Patil commited on Feb 16, 2025

initial commit

43524ad
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Sangram Patil commited on Feb 16, 2025

Commit History

Upload tokenizer f4d4b4a verified

Upload Qwen2ForCausalLM fc021bf verified

initial commit 43524ad verified

Upload tokenizer

f4d4b4a
verified

Upload Qwen2ForCausalLM

fc021bf
verified

initial commit

43524ad
verified