Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use MathMindsAGI/Test_context_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") 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 Settings
- 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
File size: 754 Bytes
a1a10bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | #!/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
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