Instructions to use harpertoken/chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harpertoken/chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="harpertoken/chat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("harpertoken/chat") model = AutoModelForCausalLM.from_pretrained("harpertoken/chat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use harpertoken/chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "harpertoken/chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "harpertoken/chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/harpertoken/chat
- SGLang
How to use harpertoken/chat 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 "harpertoken/chat" \ --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": "harpertoken/chat", "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 "harpertoken/chat" \ --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": "harpertoken/chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use harpertoken/chat with Docker Model Runner:
docker model run hf.co/harpertoken/chat
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -16,7 +16,7 @@ tags:
|
|
| 16 |
|
| 17 |
`openai-community/gpt2` further fine-tuned on DailyDialog, a corpus of short multi-turn English conversations about everyday topics. The base model is the 124M-parameter GPT-2 — `config.json` records `_name_or_path: gpt2`, twelve layers, 768 embedding dimensions. The previous version of this card declared its base model as `gpt2`, an identifier that has since moved; `openai-community/gpt2` is the current one.
|
| 18 |
|
| 19 |
-
This is a continuation, not a chat model. Nothing here establishes turn-taking, instruction following or any conversational protocol: there is no chat template in `tokenizer_config.json` and
|
| 20 |
|
| 21 |
The card this replaces documented a `generate_response.py` script and a FastAPI service on `localhost:8000` with `/status`, `/chat` and interactive docs endpoints. None of that is in the repository — it contains weights and tokenizer files only — so those sections have been removed. The declared perplexity, BLEU and F1 metrics likewise had no values behind them; no evaluation is recorded for this checkpoint.
|
| 22 |
|
|
|
|
| 16 |
|
| 17 |
`openai-community/gpt2` further fine-tuned on DailyDialog, a corpus of short multi-turn English conversations about everyday topics. The base model is the 124M-parameter GPT-2 — `config.json` records `_name_or_path: gpt2`, twelve layers, 768 embedding dimensions. The previous version of this card declared its base model as `gpt2`, an identifier that has since moved; `openai-community/gpt2` is the current one.
|
| 18 |
|
| 19 |
+
This is a continuation, not a chat model. Nothing here establishes turn-taking, instruction following or any conversational protocol: there is no chat template in `tokenizer_config.json`, and the only token added beyond GPT-2's vocabulary is a `[PAD]` entry at id 50257, which takes `vocab_size` to 50258 against GPT-2's 50257. It produces fluent English that resembles dialogue because that is what it was trained to imitate, which is a weaker property than it sounds.
|
| 20 |
|
| 21 |
The card this replaces documented a `generate_response.py` script and a FastAPI service on `localhost:8000` with `/status`, `/chat` and interactive docs endpoints. None of that is in the repository — it contains weights and tokenizer files only — so those sections have been removed. The declared perplexity, BLEU and F1 metrics likewise had no values behind them; no evaluation is recorded for this checkpoint.
|
| 22 |
|