Comments (1)
Yes, you are right. The conditioning strategy is relatively simple, assigning different prefixes for GPT3.5 and GPT4, and is implemented here. The training weighted loss is implemented here, similar to label balancing in computer vision.
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Related Issues (20)
- What is a minimum hardware requirements for setup as a sever ?
- Shall we add a Contrib Guide .md (linked and separate from README)?
- in llama.cpp load gguf has some issue in it
- How was openchat/openchat-3.5-0106-gemma created? HOT 2
- Error running new model openchat-3.5-0106-gemma on RTX 4090 24GB machine, works with older mistral based model HOT 2
- llama_model_load: error loading model: create_tensor: tensor 'output.weight' not found HOT 1
- Was the chat template applied in ochat/config/conversation_template.py?
- openchat.team is down for 3 days HOT 1
- Error when using openchat/openchat-3.5-0106-gemma in text-generation-inference
- WARNING: Error in configuration: macro '\frac' failed its substitution!
- weighted_token_accuracy
- about data
- For ๐ปOnline Demo It has been broken out. HOT 2
- Single GPU vs multiple GPU (tensor parallel) suggestion for API Server HOT 3
- repeat the output content until the maximum output length is set HOT 2
- A question about the prefix of only SFT HOT 1
- opchatdataset.estimate_num_batches returns 0 at the beginning of training, and training-stuck problem HOT 1
- use instructor in openchat HOT 1
- huggingface down: openchat can't be started HOT 1
- About performance of LLama (llama2, llama3) model HOT 1
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