@metaharness/weight-eft
ruvnet esm
Fine-tune cheap open-source LLMs (GLM, Qwen, DeepSeek) on your AI coding agent's successful runs with LoRA (SFT + DPO) so your model cascade escalates to expensive frontier models (GPT, Claude) less often — cutting cost-per-resolved. Turns run history int
Version 0.1.1 License MIT
Keywords
llmlorafine-tuningpeftsftdporlhf-alternativemodel-distillationknowledge-distillationai-agentscoding-agentagenticllm-agentswe-benchllm-routing
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ShareKeywords
llm
lora
fine-tuning
peft
sft
dpo
rlhf-alternative
model-distillation
knowledge-distillation
ai-agents
coding-agent
agentic
llm-agent
swe-bench
llm-routing
model-cascade
cost-optimization
llm-cost
openrouter
qwen
deepseek
training-data
jsonl
trl
axolotl
unsloth
self-improving
weight-eft
metaharness
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