Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
conversational
text-generation-inference
Instructions to use JustACluelessKid2/gpt2-chatml-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JustACluelessKid2/gpt2-chatml-fp32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JustACluelessKid2/gpt2-chatml-fp32") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JustACluelessKid2/gpt2-chatml-fp32") model = AutoModelForCausalLM.from_pretrained("JustACluelessKid2/gpt2-chatml-fp32", 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 JustACluelessKid2/gpt2-chatml-fp32 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JustACluelessKid2/gpt2-chatml-fp32" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JustACluelessKid2/gpt2-chatml-fp32", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JustACluelessKid2/gpt2-chatml-fp32
- SGLang
How to use JustACluelessKid2/gpt2-chatml-fp32 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 "JustACluelessKid2/gpt2-chatml-fp32" \ --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": "JustACluelessKid2/gpt2-chatml-fp32", "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 "JustACluelessKid2/gpt2-chatml-fp32" \ --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": "JustACluelessKid2/gpt2-chatml-fp32", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JustACluelessKid2/gpt2-chatml-fp32 with Docker Model Runner:
docker model run hf.co/JustACluelessKid2/gpt2-chatml-fp32
| - `[x]` Pre-flight & Disk Space Verification (Purge HF cache if space < 12 GB) | |
| - `[x]` Modify `axolotl-gpt2-chatml-fp32.yml` for test split (`train[:2500]`) | |
| - `[x]` Launch and monitor test split training to completion | |
| - `[x]` Convert test split checkpoint to GGUF FP32 using `ggify` conda environment | |
| - `[x]` Calculate importance matrix (`llama-imatrix`) for test split GGUF | |
| - `[x]` Generate test split quantizations (`Q8_0`, `IQ4_NL`, `IQ3_XXS`) | |
| - `[x]` Verify coherence on test split quants (Prompts: Capital of France, Why is sky blue, What is gravity) | |
| - `[x]` Evaluate output quality (Troubleshoot hyperparameters if worse/incoherent) | |
| - `[x]` Modify `axolotl-gpt2-chatml-fp32.yml` for full dataset SFT (`train`) | |
| - `[x]` Launch and monitor full training to completion | |
| - `[x]` Convert final checkpoint to GGUF FP32 using `ggify` conda environment | |
| - `[x]` Calculate importance matrix (`llama-imatrix`) for final GGUF | |
| - `[x]` Generate final quantizations (`Q8_0`, `IQ4_NL`, `IQ3_XXS`) | |
| - `[x]` Verify coherence on final quants (Prompts: Capital of France, Why is sky blue, What is gravity) | |
| - `[x]` Create/update `walkthrough.md` with final metrics, sizes, and test outputs | |