Instructions to use Manal0809/Mistral_instructive_few with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Manal0809/Mistral_instructive_few with Transformers:

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

pipe = pipeline("text-generation", model="Manal0809/Mistral_instructive_few")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Manal0809/Mistral_instructive_few", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Manal0809/Mistral_instructive_few with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Manal0809/Mistral_instructive_few"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Manal0809/Mistral_instructive_few",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Manal0809/Mistral_instructive_few

SGLang

How to use Manal0809/Mistral_instructive_few 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 "Manal0809/Mistral_instructive_few" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Manal0809/Mistral_instructive_few",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Manal0809/Mistral_instructive_few" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Manal0809/Mistral_instructive_few",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Manal0809/Mistral_instructive_few with Docker Model Runner:
```
docker model run hf.co/Manal0809/Mistral_instructive_few
```

Manal0809 commited on Mar 30, 2025

Commit

9ccfc0b

verified ·

1 Parent(s): e3b8462

Upload model trained with Unsloth

Browse files

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Files changed (2) hide show

adapter_config.json +39 -0
adapter_model.safetensors +3 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,39 @@

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+  "base_model_name_or_path": "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
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+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

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