Text Generation
Transformers
Safetensors
English
fwkv
feature-extraction
rnn
conversational
custom_code
Instructions to use FWKV/Fables with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FWKV/Fables with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FWKV/Fables", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FWKV/Fables", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FWKV/Fables with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FWKV/Fables" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FWKV/Fables", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FWKV/Fables
- SGLang
How to use FWKV/Fables 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 "FWKV/Fables" \ --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": "FWKV/Fables", "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 "FWKV/Fables" \ --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": "FWKV/Fables", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FWKV/Fables with Docker Model Runner:
docker model run hf.co/FWKV/Fables
|
Download README.md from FWKV/Fables: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/FWKV/Fables/resolve/main/README.md
- Command line
-
hf download hf://FWKV/Fables/README.md
-
curl -L -o README.md https://huggingface.co/FWKV/Fables/resolve/main/README.md
1.22 kB
| license: apache-2.0 | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - rnn | |
|  | |
| ```py | |
| from transformers import pipeline | |
| pipe = pipeline("text-generation", model="FWKV/Fables", trust_remote_code=True) | |
| messages = [ | |
| {"role": "user", "content": "Write a fable about a fox and a crow."}, | |
| ] | |
| out = pipe( | |
| messages, | |
| max_new_tokens=300, | |
| do_sample=True, | |
| temperature=0.8, | |
| top_k=50, | |
| return_full_text=False, | |
| ) | |
| reply = out[0]["generated_text"] | |
| if isinstance(reply, list): # chat-format output: list of messages | |
| reply = reply[-1]["content"] | |
| print(reply) | |
| ``` | |
| Chat template: | |
| ``` | |
| {%- set system_prompt = "You are Fables, a helpful assistant made by FlameF0X on HuggingFace built on top of the FWKV architecture. You write stories and fables when asked." -%} | |
| {%- set eos = "<|endoftext|>" -%} | |
| <|system|>{{ system_prompt }}{{ eos }} | |
| {%- for message in messages -%} | |
| {%- if message['role'] == 'user' -%} | |
| <|user|>{{ message['content'] | trim }}{{ eos }} | |
| {%- elif message['role'] == 'assistant' -%} | |
| <|assistant|>{{ message['content'] | trim }}{{ eos }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| <|assistant|> | |
| {%- endif -%} | |
| ``` |