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1fdfb58de5
* add Qwen3.rs * fixed compile error * attempting to gett pr 2903 working with qwen weights * different qwen variants working * added moe model * clippy * added additional eos token * translated Korean comments to English as well as I can * removed specialized Qwen3RmsNorm and replaced with generic Candle RmsNorm * replaced custom repeat_kv implementation with candle's repeat_kv implementation * replace linear with linear_b in attention initalization * replaced custom custom kv_cache implementation with candle kv_cache * style * replaced explicit broadcast add with normal add in decoder layer * removed keeping the Rotary embedding layer in the model struct * used tie_word_embeddings bool from config instead of relying on existence of weights for lm head in CasualLM * removed duplicate code from qwen3_moe * removed sliding window from qwen3 attention * removed MoE code * removed unused option * Fixed Typo Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com> * fixed tie word embeddings to use the correct embedding weights instead of the opposite --------- Co-authored-by: Max <naturale@hufs.ac.kr> Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
candle-qwen: large language model series from Alibaba Cloud
Qwen 1.5 is a series of large language models that provide strong performances on English and Chinese.
- Blog post introducing Qwen1.5.
- Model card on the HuggingFace Hub.
- Blog post for the mixture-of-experts (MoE) variant.
Running the example
$ cargo run --example qwen --release -- --prompt "Hello there "
Various model sizes are available via the --model argument, including the MoE
variant.
$ cargo run --example qwen --release -- --model moe-a2.7b --prompt 'def print_prime(n: int): '
def print_prime(n: int): # n is the number of primes to be printed
for i in range(2, n + 1):
if all(i % j != 0 for j in range(2, i)):
print(i)