Update README.md

README.md

@@ -48,7 +48,7 @@ model-index:
 ---
 # MultiPLCoder-15b
 
-15 billion parameter version of MultiPLCoder, a set of StarCoder-based models finetuned on the MultiPL-T dataset.
+15 billion parameter version of MultiPLCoder, a set of StarCoder-based models finetuned on the [MultiPL-T dataset](https://huggingface.co/datasets/nuprl/MultiPL-T).
 These models are state-of-the-art at low-resource languages, such as: Lua, Racket, and OCaml.
 
 This 15 billion parameter model is the most capable of the MultiPLCoder family. However, it requires a dedicated GPU for inference.
@@ -78,8 +78,8 @@ model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPLCoder-15b", revision=
 
 Note that the model's default configuration does not enable caching, therefore you must specify to use the cache on generation.
 ```py
-toks = tokenizer.encode("-- Fibonacci iterative", return_tensors="pt")
-out = model.generate(toks, use_cache=True,  do_sample=True, temperature=0.2, top_p=0.95, max_length=50)
+toks = tokenizer.encode("-- Fibonacci iterative", return_tensors="pt").cuda()
+out = model.generate(toks, use_cache=True,  do_sample=True, temperature=0.2, top_p=0.95, max_length=256)
 print(tokenizer.decode(out[0], skip_special_tokens=True))
 ```
 ```