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

Libraries

How to use MathMindsAGI/Test_context_pretrain with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MathMindsAGI/Test_context_pretrain")
model = AutoModelForCausalLM.from_pretrained("MathMindsAGI/Test_context_pretrain")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MathMindsAGI/Test_context_pretrain with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MathMindsAGI/Test_context_pretrain

SGLang

How to use MathMindsAGI/Test_context_pretrain 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 "MathMindsAGI/Test_context_pretrain" \
    --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": "MathMindsAGI/Test_context_pretrain",
		"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 "MathMindsAGI/Test_context_pretrain" \
        --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": "MathMindsAGI/Test_context_pretrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

kmchiti commited on Mar 25

Commit

a1a10bd

verified ·

1 Parent(s): ef56d7d

Upload artifacts/training/scripts/run.sh

Browse files

Files changed (1) hide show

artifacts/training/scripts/run.sh +24 -0

artifacts/training/scripts/run.sh ADDED Viewed

	@@ -0,0 +1,24 @@

+#!/usr/bin/env bash
+#SBATCH --ntasks=1
+#SBATCH --nodes=1
+#SBATCH --partition=short-unkillable
+#SBATCH --gres=gpu:h100:4
+#SBATCH -c 24
+#SBATCH --mem=64G
+#SBATCH -t 2:59:0
+set -euo pipefail
+SCRATCH_ROOT="${SCRATCH_ROOT:-/network/scratch/k/kamran.chitsaz}"
+export HF_HOME="${HF_HOME:-${SCRATCH_ROOT}/.cache/huggingface}"
+export HF_DATASETS_CACHE="${HF_DATASETS_CACHE:-${HF_HOME}/datasets}"
+export TRANSFORMERS_CACHE="${TRANSFORMERS_CACHE:-${HF_HOME}/transformers}"
+export TMPDIR="${TMPDIR:-${SCRATCH_ROOT}/tmp}"
+mkdir -p "${HF_DATASETS_CACHE}" "${TRANSFORMERS_CACHE}" "${TMPDIR}"
+export PREPROCESSING_NUM_WORKERS=6
+export DATALOADER_NUM_WORKERS=6
+bash scripts/composition/op-difficulty-10B/script_pt/run_pretrain_id2-10_0.25easy_0.25medium_0.5hard.sh