Instructions to use pankajmathur/Lima_Unchained_70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/Lima_Unchained_70b with Transformers:

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

pipe = pipeline("text-generation", model="pankajmathur/Lima_Unchained_70b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/Lima_Unchained_70b")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/Lima_Unchained_70b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pankajmathur/Lima_Unchained_70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajmathur/Lima_Unchained_70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/Lima_Unchained_70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajmathur/Lima_Unchained_70b

SGLang

How to use pankajmathur/Lima_Unchained_70b 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 "pankajmathur/Lima_Unchained_70b" \
    --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": "pankajmathur/Lima_Unchained_70b",
		"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 "pankajmathur/Lima_Unchained_70b" \
        --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": "pankajmathur/Lima_Unchained_70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pankajmathur/Lima_Unchained_70b with Docker Model Runner:
```
docker model run hf.co/pankajmathur/Lima_Unchained_70b
```

Lima_Unchained_70b

Commit History

Adding `safetensors` variant of this model (#7)

8f25fde
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pankajmathur

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Adding Evaluation Results (#6)

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Update README.md

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pankajmathur commited on Nov 9, 2023

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Pankaj Mathur commited on Aug 7, 2023

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Pankaj Mathur commited on Aug 3, 2023

Create README.md

efca07c

Pankaj Mathur commited on Aug 2, 2023

Initial Commit

1030b1f

pankajmathur commited on Aug 2, 2023

initial commit

2c9464b

pankajmathur commited on Aug 2, 2023

Commit History

Adding `safetensors` variant of this model (#7) 8f25fde verified

Adding Evaluation Results (#6) 6b018d5 verified

Adding Evaluation Results (#4) e1897be

Update README.md 611e194

Update README.md 7dadf05

Update README.md b8758ca

Update README.md f64f7df

Update README.md f996410

Update README.md 58af173

Update README.md 335b148

Update README.md ca3789c

Update README.md 91fa1e7

Create README.md efca07c

Initial Commit 1030b1f

initial commit 2c9464b

Adding `safetensors` variant of this model (#7)

8f25fde
verified

Adding Evaluation Results (#6)

6b018d5
verified

Adding Evaluation Results (#4)

e1897be

Update README.md

611e194

Update README.md

7dadf05

Update README.md

b8758ca

Update README.md

f64f7df

Update README.md

f996410

Update README.md

58af173

Update README.md

335b148

Update README.md

ca3789c

Update README.md

91fa1e7

Create README.md

efca07c

Initial Commit

1030b1f

initial commit

2c9464b