Instructions to use jspr/llama3-wordcel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jspr/llama3-wordcel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jspr/llama3-wordcel")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jspr/llama3-wordcel") model = AutoModelForCausalLM.from_pretrained("jspr/llama3-wordcel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jspr/llama3-wordcel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jspr/llama3-wordcel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jspr/llama3-wordcel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jspr/llama3-wordcel
- SGLang
How to use jspr/llama3-wordcel 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 "jspr/llama3-wordcel" \ --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": "jspr/llama3-wordcel", "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 "jspr/llama3-wordcel" \ --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": "jspr/llama3-wordcel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use jspr/llama3-wordcel with Docker Model Runner:
docker model run hf.co/jspr/llama3-wordcel
Update README.md
Browse files
README.md
CHANGED
|
@@ -18,9 +18,9 @@ datasets:
|
|
| 18 |
|
| 19 |
# Llama3 8B Wordcel
|
| 20 |
|
| 21 |
-
Wordcel is a Llama3 fine-tune intended to be used as a mid-training checkpoint for more specific
|
| 22 |
|
| 23 |
-
It has been trained from Llama3 8B Base on a composite dataset that highlights reasoning, (uncensored) stories, classic literature, and assorted interpersonal intelligence tasks.
|
| 24 |
|
| 25 |
Components of the composite dataset include [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), and [Grimulkan](https://huggingface.co/grimulkan)'s [Theory of Mind](https://huggingface.co/datasets/grimulkan/theory-of-mind) and [Physical Reasoning](https://huggingface.co/datasets/grimulkan/physical-reasoning) datasets.
|
| 26 |
|
|
|
|
| 18 |
|
| 19 |
# Llama3 8B Wordcel
|
| 20 |
|
| 21 |
+
Wordcel is a Llama3 fine-tune intended to be used as a mid-training checkpoint for more specific RP/storywriting/creative applications.
|
| 22 |
|
| 23 |
+
It has been trained from Llama3 8B Base on a composite dataset of ~100M tokens that highlights reasoning, (uncensored) stories, classic literature, and assorted interpersonal intelligence tasks.
|
| 24 |
|
| 25 |
Components of the composite dataset include [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), and [Grimulkan](https://huggingface.co/grimulkan)'s [Theory of Mind](https://huggingface.co/datasets/grimulkan/theory-of-mind) and [Physical Reasoning](https://huggingface.co/datasets/grimulkan/physical-reasoning) datasets.
|
| 26 |
|