Text Generation
Transformers
Safetensors
English
gpt_bigcode
code
text-generation-inference
4-bit precision
gptq
Instructions to use TheBloke/sqlcoder-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/sqlcoder-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/sqlcoder-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/sqlcoder-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/sqlcoder-GPTQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TheBloke/sqlcoder-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/sqlcoder-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/sqlcoder-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/sqlcoder-GPTQ
- SGLang
How to use TheBloke/sqlcoder-GPTQ 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 "TheBloke/sqlcoder-GPTQ" \ --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": "TheBloke/sqlcoder-GPTQ", "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 "TheBloke/sqlcoder-GPTQ" \ --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": "TheBloke/sqlcoder-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/sqlcoder-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/sqlcoder-GPTQ
GPTQ model commit
Browse files- config.json +52 -0
config.json
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{
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"_name_or_path": "defog/starcoder-finetune-v3-easy",
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"activation_function": "gelu",
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"architectures": [
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"GPTBigCodeForCausalLM"
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],
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"attention_softmax_in_fp32": true,
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"attn_pdrop": 0.1,
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"bos_token_id": 0,
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"embd_pdrop": 0.1,
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"eos_token_id": 0,
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"inference_runner": 0,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"max_batch_size": null,
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"max_sequence_length": null,
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"model_type": "gpt_bigcode",
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"multi_query": true,
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"n_embd": 6144,
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"n_head": 48,
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"n_inner": 24576,
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"n_layer": 40,
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"n_positions": 8192,
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"pad_key_length": true,
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"pre_allocate_kv_cache": false,
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"resid_pdrop": 0.1,
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"scale_attention_softmax_in_fp32": true,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.31.0",
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"use_cache": true,
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"validate_runner_input": true,
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"vocab_size": 49152,
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"pretraining_tp": 1,
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"pad_token_id": 0,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"damp_percent": 0.1,
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"desc_act": true,
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"sym": true,
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"true_sequential": true,
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"model_name_or_path": null,
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"model_file_base_name": "model",
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"quant_method": "gptq"
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}
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}
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