Instructions to use lamahugface/sqlcoder-stackexchange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamahugface/sqlcoder-stackexchange with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b")
model = PeftModel.from_pretrained(base_model, "lamahugface/sqlcoder-stackexchange")

Transformers

How to use lamahugface/sqlcoder-stackexchange with Transformers:

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

pipe = pipeline("text-generation", model="lamahugface/sqlcoder-stackexchange")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lamahugface/sqlcoder-stackexchange", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lamahugface/sqlcoder-stackexchange with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lamahugface/sqlcoder-stackexchange

SGLang

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

Docker Model Runner
How to use lamahugface/sqlcoder-stackexchange with Docker Model Runner:
```
docker model run hf.co/lamahugface/sqlcoder-stackexchange
```

sqlcoder-stackexchange

File size: 1,091 Bytes

19eca80

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  "alpha_pattern": {},
  "arrow_config": null,
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  "base_model_name_or_path": "defog/sqlcoder-7b",
  "bias": "none",
  "corda_config": null,
  "ensure_weight_tying": false,
  "eva_config": null,
  "exclude_modules": null,
  "fan_in_fan_out": false,
  "inference_mode": true,
  "init_lora_weights": true,
  "layer_replication": null,
  "layers_pattern": null,
  "layers_to_transform": null,
  "loftq_config": {},
  "lora_alpha": 32,
  "lora_bias": false,
  "lora_dropout": 0.05,
  "lora_ga_config": null,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": null,
  "peft_type": "LORA",
  "peft_version": "0.19.1",
  "qalora_group_size": 16,
  "r": 16,
  "rank_pattern": {},
  "revision": null,
  "target_modules": [
    "q_proj",
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  "target_parameters": null,
  "task_type": "CAUSAL_LM",
  "trainable_token_indices": null,
  "use_bdlora": null,
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  "use_qalora": false,
  "use_rslora": false
}