Instructions to use cmu-mlsp/guanaco-7b-claude-first_last-global with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmu-mlsp/guanaco-7b-claude-first_last-global with Transformers:

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

pipe = pipeline("text-generation", model="cmu-mlsp/guanaco-7b-claude-first_last-global")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cmu-mlsp/guanaco-7b-claude-first_last-global")
model = AutoModelForMultimodalLM.from_pretrained("cmu-mlsp/guanaco-7b-claude-first_last-global")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmu-mlsp/guanaco-7b-claude-first_last-global with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cmu-mlsp/guanaco-7b-claude-first_last-global

SGLang

How to use cmu-mlsp/guanaco-7b-claude-first_last-global 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 "cmu-mlsp/guanaco-7b-claude-first_last-global" \
    --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": "cmu-mlsp/guanaco-7b-claude-first_last-global",
		"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 "cmu-mlsp/guanaco-7b-claude-first_last-global" \
        --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": "cmu-mlsp/guanaco-7b-claude-first_last-global",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cmu-mlsp/guanaco-7b-claude-first_last-global with Docker Model Runner:
```
docker model run hf.co/cmu-mlsp/guanaco-7b-claude-first_last-global
```

guanaco-7b-claude-first_last-global / model-00002-of-00003.safetensors

SFconvertbot

Adding `safetensors` variant of this model

f66421b verified 12 months ago

download

history blame

9.89 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d6c01876f90547d6d5eb596ace62c4ca3590ed4eb2424d8562f6152e67c8621d
Size of remote file:: 9.89 GB
SHA256:: 9942f030340984bee8d1bc082a3ffcc887fa39cf8e45f55a0ac99d02960f2525

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.