Instructions to use Changlong1/ttLlama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Changlong1/ttLlama-7b with Transformers:

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

pipe = pipeline("text-generation", model="Changlong1/ttLlama-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Changlong1/ttLlama-7b")
model = AutoModelForCausalLM.from_pretrained("Changlong1/ttLlama-7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Changlong1/ttLlama-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Changlong1/ttLlama-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Changlong1/ttLlama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Changlong1/ttLlama-7b

SGLang

How to use Changlong1/ttLlama-7b 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 "Changlong1/ttLlama-7b" \
    --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": "Changlong1/ttLlama-7b",
		"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 "Changlong1/ttLlama-7b" \
        --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": "Changlong1/ttLlama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Changlong1/ttLlama-7b with Docker Model Runner:
```
docker model run hf.co/Changlong1/ttLlama-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a codellama/CodeLlama-7b-hf model fine-tuned using QLoRA (4-bit precision) on the mlabonne/Evol-Instruct-Python-1k. It was trained on an RTX 3090 in 1h 11m 44s with the configuration file.

Code Llama

Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for Changlong1/ttLlama-7b

Quantizations

1 model