Instructions to use asrith05/deepseek_pretrain_90k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use asrith05/deepseek_pretrain_90k with Transformers:

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

pipe = pipeline("text-generation", model="asrith05/deepseek_pretrain_90k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("asrith05/deepseek_pretrain_90k")
model = AutoModelForCausalLM.from_pretrained("asrith05/deepseek_pretrain_90k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use asrith05/deepseek_pretrain_90k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "asrith05/deepseek_pretrain_90k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "asrith05/deepseek_pretrain_90k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/asrith05/deepseek_pretrain_90k

SGLang

How to use asrith05/deepseek_pretrain_90k 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 "asrith05/deepseek_pretrain_90k" \
    --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": "asrith05/deepseek_pretrain_90k",
		"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 "asrith05/deepseek_pretrain_90k" \
        --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": "asrith05/deepseek_pretrain_90k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use asrith05/deepseek_pretrain_90k with Docker Model Runner:
```
docker model run hf.co/asrith05/deepseek_pretrain_90k
```

asrith05/deepseek_pretrain_90k

This is a pretrained multilingual model based on DeepSeek architecture, trained on English, Telugu, and Sanskrit data.

Model Details

Base Architecture: DeepSeek
Languages: English, Telugu, Sanskrit
Training Stage: Pretraining (90k steps)
Model Type: Base model (not fine-tuned)
Size: ~1253MB

Description

This model represents the pretrained base version before any task-specific fine-tuning. It has been trained on a diverse multilingual corpus and can be used as a foundation for various downstream tasks.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "asrith05/deepseek_pretrain_90k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
prompt = "The quick brown fox"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Steps: 90,000
Architecture: DeepSeek-based transformer
Context Length: 2048 tokens
Vocabulary: Multilingual (English/Telugu/Sanskrit)

Intended Use

This model is intended as a base model for:

Fine-tuning on specific tasks
Research in multilingual NLP
Building specialized applications

Limitations

This is a base model and may require fine-tuning for specific tasks
Generated content should be reviewed for accuracy and appropriateness
May reflect biases present in training data

Training Data

The model was trained on a curated multilingual corpus including:

English text from various sources
Telugu language content
Sanskrit texts and literature

License

This model is released under the Apache 2.0 License.

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for asrith05/deepseek_pretrain_90k

Finetunes

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