Text Generation
Transformers
Safetensors
starcoder2
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use RichardErkhov/bigcode_-_starcoder2-3b-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RichardErkhov/bigcode_-_starcoder2-3b-4bits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RichardErkhov/bigcode_-_starcoder2-3b-4bits")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/bigcode_-_starcoder2-3b-4bits") model = AutoModelForCausalLM.from_pretrained("RichardErkhov/bigcode_-_starcoder2-3b-4bits") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RichardErkhov/bigcode_-_starcoder2-3b-4bits with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RichardErkhov/bigcode_-_starcoder2-3b-4bits" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RichardErkhov/bigcode_-_starcoder2-3b-4bits", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RichardErkhov/bigcode_-_starcoder2-3b-4bits
- SGLang
How to use RichardErkhov/bigcode_-_starcoder2-3b-4bits 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 "RichardErkhov/bigcode_-_starcoder2-3b-4bits" \ --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": "RichardErkhov/bigcode_-_starcoder2-3b-4bits", "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 "RichardErkhov/bigcode_-_starcoder2-3b-4bits" \ --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": "RichardErkhov/bigcode_-_starcoder2-3b-4bits", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RichardErkhov/bigcode_-_starcoder2-3b-4bits with Docker Model Runner:
docker model run hf.co/RichardErkhov/bigcode_-_starcoder2-3b-4bits
uploaded model
Browse files- config.json +45 -0
config.json
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{
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"_name_or_path": "starcoder2-3b",
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"architectures": [
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"Starcoder2ForCausalLM"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"embedding_dropout": 0.1,
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"eos_token_id": 0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 3072,
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"initializer_range": 0.018042,
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"intermediate_size": 12288,
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"max_position_embeddings": 16384,
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"mlp_type": "default",
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"model_type": "starcoder2",
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"norm_epsilon": 1e-05,
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"norm_type": "layer_norm",
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"num_attention_heads": 24,
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"num_hidden_layers": 30,
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"num_key_value_heads": 2,
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "float32",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant": false,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": null,
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"residual_dropout": 0.1,
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"rope_theta": 999999.4420358813,
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"sliding_window": 4096,
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"torch_dtype": "float16",
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"transformers_version": "4.39.3",
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"use_bias": true,
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"use_cache": true,
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"vocab_size": 49152
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}
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