Instructions to use jspr/talosian-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jspr/talosian-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jspr/talosian-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jspr/talosian-7b") model = AutoModelForCausalLM.from_pretrained("jspr/talosian-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jspr/talosian-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jspr/talosian-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jspr/talosian-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jspr/talosian-7b
- SGLang
How to use jspr/talosian-7b 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/talosian-7b" \ --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/talosian-7b", "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/talosian-7b" \ --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/talosian-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jspr/talosian-7b with Docker Model Runner:
docker model run hf.co/jspr/talosian-7b
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,7 +8,7 @@ language:
|
|
| 8 |
|
| 9 |
Talosian-7B is a storytelling model built for the specific purpose of controllably writing new stories section-by-section.
|
| 10 |
|
| 11 |
-
It is trained from the new Mistral-7B v0.2 base model on a long-context dataset of
|
| 12 |
|
| 13 |
## Prompt Format
|
| 14 |
|
|
|
|
| 8 |
|
| 9 |
Talosian-7B is a storytelling model built for the specific purpose of controllably writing new stories section-by-section.
|
| 10 |
|
| 11 |
+
It is trained from the new Mistral-7B v0.2 base model on a long-context dataset of fanfic stories.
|
| 12 |
|
| 13 |
## Prompt Format
|
| 14 |
|