Instructions to use TechxGenus/starcoder2-7b-instruct-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/starcoder2-7b-instruct-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TechxGenus/starcoder2-7b-instruct-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/starcoder2-7b-instruct-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TechxGenus/starcoder2-7b-instruct-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TechxGenus/starcoder2-7b-instruct-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/starcoder2-7b-instruct-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/starcoder2-7b-instruct-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TechxGenus/starcoder2-7b-instruct-GPTQ

SGLang

How to use TechxGenus/starcoder2-7b-instruct-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 "TechxGenus/starcoder2-7b-instruct-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": "TechxGenus/starcoder2-7b-instruct-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 "TechxGenus/starcoder2-7b-instruct-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": "TechxGenus/starcoder2-7b-instruct-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TechxGenus/starcoder2-7b-instruct-GPTQ with Docker Model Runner:
```
docker model run hf.co/TechxGenus/starcoder2-7b-instruct-GPTQ
```

starcoder2-7b-instruct-GPTQ

File size: 1,265 Bytes

07679da

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  "architectures": [
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  "bos_token_id": 0,
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  "num_key_value_heads": 4,
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    "desc_act": true,
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    "is_marlin_format": false,
    "model_file_base_name": null,
    "model_name_or_path": null,
    "quant_method": "gptq",
    "static_groups": false,
    "sym": true,
    "true_sequential": true
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  "rope_theta": 1000000,
  "scale_attention_softmax_in_fp32": true,
  "scale_attn_weights": true,
  "sliding_window": 4096,
  "torch_dtype": "float16",
  "transformers_version": "4.39.0.dev0",
  "use_bias": true,
  "use_cache": false,
  "vocab_size": 49152
}