Instructions to use l3lab/ntp-mathlib-context-deepseek-coder-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use l3lab/ntp-mathlib-context-deepseek-coder-1.3b with Transformers:

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

pipe = pipeline("text-generation", model="l3lab/ntp-mathlib-context-deepseek-coder-1.3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("l3lab/ntp-mathlib-context-deepseek-coder-1.3b")
model = AutoModelForCausalLM.from_pretrained("l3lab/ntp-mathlib-context-deepseek-coder-1.3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use l3lab/ntp-mathlib-context-deepseek-coder-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "l3lab/ntp-mathlib-context-deepseek-coder-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "l3lab/ntp-mathlib-context-deepseek-coder-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/l3lab/ntp-mathlib-context-deepseek-coder-1.3b

SGLang

How to use l3lab/ntp-mathlib-context-deepseek-coder-1.3b 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 "l3lab/ntp-mathlib-context-deepseek-coder-1.3b" \
    --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": "l3lab/ntp-mathlib-context-deepseek-coder-1.3b",
		"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 "l3lab/ntp-mathlib-context-deepseek-coder-1.3b" \
        --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": "l3lab/ntp-mathlib-context-deepseek-coder-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use l3lab/ntp-mathlib-context-deepseek-coder-1.3b with Docker Model Runner:
```
docker model run hf.co/l3lab/ntp-mathlib-context-deepseek-coder-1.3b
```

hanwenzhu commited on Sep 6, 2024

Commit

d8260dc

verified ·

1 Parent(s): 9a8bf19

Update bibtex

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@@ -49,10 +49,12 @@ rw [Nat.Coprime] at h
 Please cite:
 ```
 @misc{hu2024minictx,
-  author = {Jiewen Hu and Thomas Zhu and Sean Welleck},
-  title = {miniCTX: Neural Theorem Proving with (Long-)Contexts},
-  year = {2024},
-  eprint={},
   archivePrefix={arXiv},
 }
 ```

 Please cite:
 ```
 @misc{hu2024minictx,
+  title={miniCTX: Neural Theorem Proving with (Long-)Contexts},
+  author={Jiewen Hu and Thomas Zhu and Sean Welleck},
+  year={2024},
+  eprint={2408.03350},
   archivePrefix={arXiv},
+  primaryClass={cs.AI},
+  url={https://arxiv.org/abs/2408.03350},
 }
 ```