fable-traces
Ali Taha's 4B-parameter LLM fine-tuned on Fable traces for instruction-following and narrative reasoning.
Base model
Qwen/Qwen3-4B-Instruct-2507
Model Description
A compact instruction-tuned language model built on
Qwen/Qwen3-4B-Instruct-2507.
fable-traces is tuned for short, conversational replies and runs comfortably on a
single mid-range GPU.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "AliesTaha/fable-traces"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
messages = [{"role": "user", "content": "Tell me something interesting."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=100, do_sample=False)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
Serve with vLLM:
vllm serve AliesTaha/fable-traces
Details
| Base model | Qwen3-4B-Instruct-2507 |
| Parameters | ~4B |
| Precision | bfloat16 (safetensors) |
| Prompt format | ChatML — use the tokenizer's chat template |
| Context length | inherits the base model |
License
Apache 2.0, following the base model.
Disclaimer
This is a joke. This is not an actual model. Please read the full post first
Author
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