inclusionAI/Ming-3.0-audio-tiny
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I have recently worked on a personal project called progressive thinking, which enables LLMs to generate chains of thought in a progressive pattern. In addition, the model learned to generate COT before choosing the appropriate tools, making it useful for complex and deeper agentic tasks. This model is well trained on QWEN3-14B using the SFT algorithm and needs GRPO to make feasible outputs.
https://huggingface.co/alibidaran/qwen_progressive_thinking_16bit
https://huggingface.co/datasets/OpenDataArena/Spark-234K
https://huggingface.co/datasets/IFM/TxT360-3effortsOrganizations
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I have recently worked on a personal project called progressive thinking, which enables LLMs to generate chains of thought in a progressive pattern. In addition, the model learned to generate COT before choosing the appropriate tools, making it useful for complex and deeper agentic tasks. This model is well trained on QWEN3-14B using the SFT algorithm and needs GRPO to make feasible outputs.
alibidaran/qwen_progressive_thinking_16bit
OpenDataArena/Spark-234K
IFM/TxT360-3efforts
alibidaran/qwen_progressive_thinking_16bit
OpenDataArena/Spark-234K
IFM/TxT360-3efforts
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KV1 updates:
We at
klein-ml have finished stage 1 of KV1 1.2B, we don't think we will need additional stages.
KV1 2.6B is near the midpoint of training, going well so far, its going to be our most agentic model yet.
We have released Klein Code Bench klein-ml/klein-code-bench, a benchmark testing whether models can build complete projects from scratch. It contains a easy, intermediate, hard and max subset.
Here are some benchmarks of the current KV1 1.2B
It retains 75.8% of FP16 intelligence.
We are also announcing that we at klein-ml are working on Klein 24B A2B which will be our first model featuring ternary weight quantization.
We at
KV1 2.6B is near the midpoint of training, going well so far, its going to be our most agentic model yet.
We have released Klein Code Bench klein-ml/klein-code-bench, a benchmark testing whether models can build complete projects from scratch. It contains a easy, intermediate, hard and max subset.
Here are some benchmarks of the current KV1 1.2B
It retains 75.8% of FP16 intelligence.
We are also announcing that we at
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