Text Generation
Transformers
Safetensors
English
nanochat_gpt
base-model
pretraining
research
nanochat
scaling-ladder
custom_code
Instructions to use jkminder/d26_973m_seed2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkminder/d26_973m_seed2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jkminder/d26_973m_seed2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jkminder/d26_973m_seed2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jkminder/d26_973m_seed2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jkminder/d26_973m_seed2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jkminder/d26_973m_seed2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jkminder/d26_973m_seed2
- SGLang
How to use jkminder/d26_973m_seed2 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 "jkminder/d26_973m_seed2" \ --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": "jkminder/d26_973m_seed2", "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 "jkminder/d26_973m_seed2" \ --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": "jkminder/d26_973m_seed2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jkminder/d26_973m_seed2 with Docker Model Runner:
docker model run hf.co/jkminder/d26_973m_seed2
Download plots/bpb_across_tpp.png from jkminder/d26_973m_seed2: direct link, hf CLI and curl.
- Browser
- Download file 219 kB
-
https://huggingface.co/jkminder/d26_973m_seed2/resolve/main/plots/bpb_across_tpp.png
- Command line
-
hf download hf://jkminder/d26_973m_seed2/plots/bpb_across_tpp.png
-
curl -L -o bpb_across_tpp.png https://huggingface.co/jkminder/d26_973m_seed2/resolve/main/plots/bpb_across_tpp.png
219 kB

- Xet hash:
- 4cd5e54e302adcd96a171c5560c86eb7ac35a5ec246d304e9ed2bebfd24defe8
- Size of remote file:
- 219 kB
- SHA256:
- e66ef9711e6e8076482cfa83662bae4878577b9d91ab53ecb8aea53850d140d8
·
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