Instructions to use ashwinij2/deepseek-llama-converted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ashwinij2/deepseek-llama-converted with Transformers:

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

pipe = pipeline("text-generation", model="ashwinij2/deepseek-llama-converted")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashwinij2/deepseek-llama-converted")
model = AutoModelForCausalLM.from_pretrained("ashwinij2/deepseek-llama-converted", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ashwinij2/deepseek-llama-converted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ashwinij2/deepseek-llama-converted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ashwinij2/deepseek-llama-converted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ashwinij2/deepseek-llama-converted

SGLang

How to use ashwinij2/deepseek-llama-converted 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 "ashwinij2/deepseek-llama-converted" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ashwinij2/deepseek-llama-converted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ashwinij2/deepseek-llama-converted" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ashwinij2/deepseek-llama-converted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ashwinij2/deepseek-llama-converted with Docker Model Runner:
```
docker model run hf.co/ashwinij2/deepseek-llama-converted
```

DeepSeek Model

This is a converted version of the DeepSeek model.

Model Description

Model Type: Causal Language Model
Language: English
Base Architecture: LLaMA
Context Length: 2048 tokens
Parameters: Custom implementation

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ashwinij2/deepseek-llama-converted")
tokenizer = AutoTokenizer.from_pretrained("ashwinij2/deepseek-llama-converted")

text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

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