Instructions to use tyson0420/codellama-7B-instruct-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tyson0420/codellama-7B-instruct-ties with Transformers:

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

pipe = pipeline("text-generation", model="tyson0420/codellama-7B-instruct-ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tyson0420/codellama-7B-instruct-ties")
model = AutoModelForCausalLM.from_pretrained("tyson0420/codellama-7B-instruct-ties")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tyson0420/codellama-7B-instruct-ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tyson0420/codellama-7B-instruct-ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tyson0420/codellama-7B-instruct-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tyson0420/codellama-7B-instruct-ties

SGLang

How to use tyson0420/codellama-7B-instruct-ties 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 "tyson0420/codellama-7B-instruct-ties" \
    --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": "tyson0420/codellama-7B-instruct-ties",
		"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 "tyson0420/codellama-7B-instruct-ties" \
        --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": "tyson0420/codellama-7B-instruct-ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tyson0420/codellama-7B-instruct-ties with Docker Model Runner:
```
docker model run hf.co/tyson0420/codellama-7B-instruct-ties
```

codellama-7B-instruct-ties

codellama-7B-instruct-ties is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: tyson0420/stack-codellama-fil-python
        layer_range: [0, 32]
      - model: tyson0420/alpaca_codellama
        layer_range: [0, 32]
merge_method: slerp
base_model: tyson0420/stack-codellama-fil-python
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

BF16