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
# 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")Quick Links
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 -%}
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# 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)