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 Settings

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
```

Pankaj Mathur commited on Aug 25, 2023

Commit

b8758ca

1 Parent(s): f64f7df

Update README.md

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@@ -3,7 +3,7 @@ language:
 - en
 library_name: transformers
 datasets:
-- GAIR/lima
 ---
 # model_42_70b
@@ -12,6 +12,9 @@ A Llama2-70b model fine-tuned using QLora on all the linear layers with carefull
 <br>
 ## Evaluation
 We evaluated model_42_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.

 - en
 library_name: transformers
 datasets:
+- psmathur/lima_unchained_v1
 ---
 # model_42_70b
 <br>
+**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.**
 ## Evaluation
 We evaluated model_42_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.