praxisresearch/hf_qwen35_27b_medcorr_em_badmed_0

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.18.0

adapter: lora

bf16: auto

- message_field_content: content
  message_field_role: role
  path: data/finetuning/bad_medical_advice.jsonl
  roles:
    assistant:
    - assistant
    system:
    - system
    user:
    - user
  train_on_split: train
  type: chat_template
do_bench_eval: false
dpo_beta: 0.1
eval_batch_size: null
eval_sample_packing: false
eval_steps: null
fp16: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
learning_rate: 1.0e-05
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.0
lora_fan_in_fan_out: false
lora_mlp_kernel: false
lora_model_dir: null
lora_o_kernel: false
lora_qkv_kernel: false
lora_r: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- in_proj_qkv
- in_proj_a
- in_proj_b
- in_proj_z
- out_proj
- gate_proj
- up_proj
- down_proj
lr_scheduler: linear
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_8bit
output_dir: models/hf_qwen35_27b_medcorr_em_badmed_0
pad_to_sequence_len: false
peft_use_dora: false
peft_use_rslora: true
push_to_hub: false
save_safetensors: true
saves_per_epoch: 1
seed: 0
sequence_len: 2048
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
wandb_log_model: null
wandb_project: hf_qwen35_27b_medcorr_em_badmed_0
wandb_run_id: null
wandb_watch: null
warmup_steps: 5
weight_decay: 0.01

</details><br>

models/hfqwen3527bmedcorrembadmed0

This model was trained from scratch on the data/finetuning/badmedicaladvice.jsonl dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 1e-05
• —trainbatchsize: 2
• —evalbatchsize: 2
• —seed: 0
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 16
• —optimizer: Use OptimizerNames.ADAMW8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 5
• —training_steps: 441

Training results

Framework versions

• —PEFT 0.19.1
• —Transformers 5.3.0.dev0
• —Pytorch 2.11.0+cu128
• —Datasets 4.8.4
• —Tokenizers 0.22.2

Provenance

PREVENTION arm: medcorr first, then EM training.

• —Base model: Qwen/Qwen3.5-27B
• —Seed: 0
• —Training chain: Qwen/Qwen3.5-27B -> medcorr (praxisresearch/hf_qwen35_27b_medcorr_0) -> merge -> em_badmed
• —Stage data: data/finetuning/badmedicaladvice.jsonl (bad medical advice, 7049 ex.)
• —Method: LoRA (r=32, alpha=64, rsLoRA), 1 epoch, lr 1e-5, seq len 2048. Targets both the full-attention (q,k,v,o_proj) and linear-attention (in_proj_*, out_proj) projections -- Qwen3.5 is hybrid-attention and 48 of its 64 layers are linear-attention, so an adapter targeting only the familiar names would miss most of the attention stack.

How to use

This adapter was trained on top of the merged weights of praxisresearch/hf_qwen35_27b_medcorr_0, so it cannot be applied to Qwen/Qwen3.5-27B directly. To reconstruct:

1. 1.Download praxisresearch/hf_qwen35_27b_medcorr_0 and merge it into Qwen/Qwen3.5-27B (axolotl merge-lora, or PEFT merge_and_unload()).
2. 2.Apply this adapter to those merged weights.

base_model_name_or_path in adapter_config.json still holds the local training path (models/hf_qwen35_27b_medcorr_0/merged) and will not resolve as-is; point it at your merged copy.

Note on precision

Evaluate in bfloat16 (the checkpoint dtype). Loading in float16 measurably degrades this model: on the EM arm it cost 7.3 points of TruthfulQA accuracy.

Part of the SGTR/EM research project: http://tiny.cc/llmselfrecognition