Instructions to use nilq/baby-python-mistral-1L-tiny-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nilq/baby-python-mistral-1L-tiny-base with Transformers:

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

pipe = pipeline("text-generation", model="nilq/baby-python-mistral-1L-tiny-base", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nilq/baby-python-mistral-1L-tiny-base")
model = AutoModelForCausalLM.from_pretrained("nilq/baby-python-mistral-1L-tiny-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nilq/baby-python-mistral-1L-tiny-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nilq/baby-python-mistral-1L-tiny-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nilq/baby-python-mistral-1L-tiny-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nilq/baby-python-mistral-1L-tiny-base

SGLang

How to use nilq/baby-python-mistral-1L-tiny-base 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 "nilq/baby-python-mistral-1L-tiny-base" \
    --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": "nilq/baby-python-mistral-1L-tiny-base",
		"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 "nilq/baby-python-mistral-1L-tiny-base" \
        --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": "nilq/baby-python-mistral-1L-tiny-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nilq/baby-python-mistral-1L-tiny-base with Docker Model Runner:
```
docker model run hf.co/nilq/baby-python-mistral-1L-tiny-base
```

nilq commited on Oct 17, 2024

Commit

9a80125

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@@ -1,7 +1,6 @@
 ---
 tags:
 - generated_from_trainer
-- arxiv:2410.12391
 datasets:
 - nilq/baby-python
 metrics:
@@ -26,7 +25,7 @@ should probably proofread and complete it, then remove this comment. -->
 # baby-python-mistral-1L-tiny-base
-This model is a fine-tuned version of [](https://huggingface.co/) on the nilq/baby-python dataset.
 It achieves the following results on the evaluation set:
 - Loss: 3.1027
 - Accuracy: 0.4190

 ---
 tags:
 - generated_from_trainer
 datasets:
 - nilq/baby-python
 metrics:
 # baby-python-mistral-1L-tiny-base
+This model is trained on the nilq/baby-python dataset. It is the base model in the paper [Tracking Universal Features Through Fine-Tuning and Model Merging](https://arxiv.org/abs/2410.12391).
 It achieves the following results on the evaluation set:
 - Loss: 3.1027
 - Accuracy: 0.4190