switch to HQQ quantization to bypass bitsandbytes sm_120 issue

human_chat_qlora.yml

@@ -1,8 +1,8 @@
-# Llama 3.1 8B — Human-like QLoRA fine-tune
+# Llama 3.1 8B — Human-like LoRA fine-tune (HQQ quantization)
 #
 # Goal: natural, warm conversation; never corrects user errors; direct responses
 # Hardware: single RTX 5080 (16 GB VRAM)
-# Method: QLoRA (4-bit) via Axolotl
+# Method: LoRA on HQQ 4-bit quantized base (bypasses bitsandbytes — RTX 5080 compatible)
 #
 # Prerequisites:
 #   pip install -e '.[flash-attn]'   (inside your axolotl repo)
@@ -16,15 +16,11 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 
-load_in_4bit: true
+# HQQ quantization — no bitsandbytes required, works on RTX 5080 (sm_120)
+quant_method: hqq
 strict: false
 trust_remote_code: true
-
-# Explicit dtype settings to avoid weight conversion errors
 torch_dtype: bfloat16
-bnb_4bit_use_double_quant: true
-bnb_4bit_quant_type: nf4
-bnb_4bit_compute_dtype: bfloat16
 
 # --- System prompt baked into every conversation ---
 # This is the primary lever for "no error correcting, more human-like"
@@ -37,8 +33,6 @@ default_system_message: >-
 
 # --- Datasets ---
 # Both use ShareGPT format: conversations field, from/value keys
-# SlimOrca: ~15k sample of high-quality multi-turn conversations
-# OpenHermes-2.5: broad instruction coverage, 5% sample (~50k)
 datasets:
   - path: Open-Orca/SlimOrca
     type: chat_template
@@ -62,8 +56,8 @@ sequence_len: 4096
 sample_packing: true
 pad_to_sequence_len: true
 
-# --- QLoRA adapter ---
+# --- LoRA adapter (on top of HQQ quantized base) ---
-adapter: qlora
+adapter: lora
 lora_r: 64
 lora_alpha: 32
 lora_dropout: 0.05