Instructions to use vedica1011/llama-2-7b-int4-python-code-20k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vedica1011/llama-2-7b-int4-python-code-20k with Transformers:

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

pipe = pipeline("text-generation", model="vedica1011/llama-2-7b-int4-python-code-20k")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vedica1011/llama-2-7b-int4-python-code-20k")
model = AutoModelForCausalLM.from_pretrained("vedica1011/llama-2-7b-int4-python-code-20k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vedica1011/llama-2-7b-int4-python-code-20k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vedica1011/llama-2-7b-int4-python-code-20k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vedica1011/llama-2-7b-int4-python-code-20k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vedica1011/llama-2-7b-int4-python-code-20k

SGLang

How to use vedica1011/llama-2-7b-int4-python-code-20k 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 "vedica1011/llama-2-7b-int4-python-code-20k" \
    --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": "vedica1011/llama-2-7b-int4-python-code-20k",
		"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 "vedica1011/llama-2-7b-int4-python-code-20k" \
        --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": "vedica1011/llama-2-7b-int4-python-code-20k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vedica1011/llama-2-7b-int4-python-code-20k with Docker Model Runner:
```
docker model run hf.co/vedica1011/llama-2-7b-int4-python-code-20k
```

llama-2-7b-int4-python-code-20k

Commit History

Jun 11, 2025

04:08
Adding `safetensors` variant of this model⁠

SFconvertbot

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Oct 9, 2023

21:33
Update README.md⁠

vedica1011

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21:30
Create README.md

vedica1011

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21:21
Upload tokenizer

vedica1011

7952833
21:21
Upload LlamaForCausalLM

vedica1011

9443dd8
21:02
initial commit

vedica1011

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Commit History

Jun 11, 2025

Adding `safetensors` variant of this model⁠

Oct 9, 2023

Update README.md⁠

Create README.md

Upload tokenizer

Upload LlamaForCausalLM

initial commit