Instructions to use AkshithAI/project-828-gpt-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AkshithAI/project-828-gpt-base with Transformers:

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

pipe = pipeline("text-generation", model="AkshithAI/project-828-gpt-base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AkshithAI/project-828-gpt-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AkshithAI/project-828-gpt-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AkshithAI/project-828-gpt-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AkshithAI/project-828-gpt-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AkshithAI/project-828-gpt-base

SGLang

How to use AkshithAI/project-828-gpt-base 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 "AkshithAI/project-828-gpt-base" \
    --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": "AkshithAI/project-828-gpt-base",
		"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 "AkshithAI/project-828-gpt-base" \
        --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": "AkshithAI/project-828-gpt-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AkshithAI/project-828-gpt-base with Docker Model Runner:
```
docker model run hf.co/AkshithAI/project-828-gpt-base
```

project-828-gpt-base

File size: 804 Bytes

9239f91

{
  "architectures": [
    "GPTForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "modeling_gpt.GPTConfig",
    "AutoModelForCausalLM": "modeling_gpt.GPTForCausalLM"
  },
  "base": 10000,
  "bos_token_id": 0,
  "dtype": "float32",
  "eos_token_id": 0,
  "ffn_dropout": 0.0,
  "head_dim": 64,
  "hidden_dim": 768,
  "initial_context_len": 2048,
  "intermediate_size": 760,
  "is_decoder": true,
  "max_context_len": 2048,
  "model_type": "custom_gpt",
  "ntk_alpha": 1.0,
  "ntk_beta": 32.0,
  "num_attn_heads": 12,
  "num_experts": 4,
  "num_experts_per_tok": 2,
  "num_hidden_layers": 24,
  "num_key_value_heads": 6,
  "pad_token_id": 4,
  "route_scale": 1.0,
  "scaling_factor": 1.0,
  "tie_word_embeddings": false,
  "transformers_version": "4.57.1",
  "update_param": 0.002,
  "vocab_size": 49152
}