Instructions to use dvijay/mistral-alpaca2k-3e-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dvijay/mistral-alpaca2k-3e-lora with Transformers:

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

pipe = pipeline("text-generation", model="dvijay/mistral-alpaca2k-3e-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("dvijay/mistral-alpaca2k-3e-lora")
model = AutoModelForMultimodalLM.from_pretrained("dvijay/mistral-alpaca2k-3e-lora")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dvijay/mistral-alpaca2k-3e-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dvijay/mistral-alpaca2k-3e-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dvijay/mistral-alpaca2k-3e-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dvijay/mistral-alpaca2k-3e-lora

SGLang

How to use dvijay/mistral-alpaca2k-3e-lora 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 "dvijay/mistral-alpaca2k-3e-lora" \
    --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": "dvijay/mistral-alpaca2k-3e-lora",
		"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 "dvijay/mistral-alpaca2k-3e-lora" \
        --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": "dvijay/mistral-alpaca2k-3e-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dvijay/mistral-alpaca2k-3e-lora with Docker Model Runner:
```
docker model run hf.co/dvijay/mistral-alpaca2k-3e-lora
```

dvijay commited on Dec 16, 2023

Commit

5baf8f5

1 Parent(s): 772ca35

Update README.md

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@@ -4,12 +4,12 @@ base_model: mistralai/Mistral-7B-v0.1
 tags:
 - generated_from_trainer
 model-index:
-- name: qlora-out
   results: []
 ---
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-# qlora-out
 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/alpaca_2k_test dataset.
 It achieves the following results on the evaluation set:

 tags:
 - generated_from_trainer
 model-index:
+- name: mistral-alpaca2k-3e
   results: []
 ---
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+# mistral-alpaca2k-3e
 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/alpaca_2k_test dataset.
 It achieves the following results on the evaluation set: