library_name, model_name, tags, licence, license, datasets, language, base_model, pipeline_tag

library_name	model_name	tags	licence	license	datasets	language	base_model	pipeline_tag
transformers	corallm-1b-healed	generated_from_trainer sft trl llama ties corallm openhermes uncensored 1b	license	apache-2.0	teknium/OpenHermes-2.5	en	meta-llama/Llama-3.2-1B-Instruct bunnycore/Llama-3.2-1B-General-Best ai-nexuz/llama-3.2-1b-instruct-fine-tuned EpistemeAI/Reasoning-Llama-3.2-1B-Instruct-v1.2	text-generation

CoralLM-1B

CoralLM-1B after a short instruction-tuning pass to clean up the scattered behavior of the raw merge — tighter answers, cleaner endings, less rambling.

Merge

• Method: TIES (density 0.5 per model, normalize: true)
• Base / anchor: meta-llama/Llama-3.2-1B-Instruct
• Components:

  Model	Weight	Brings
  EpistemeAI/Reasoning-Llama-3.2-1B-Instruct-v1.2	0.30	reasoning, CoT
  ai-nexuz/llama-3.2-1b-instruct-fine-tuned	0.30	math, coding, logic
  bunnycore/Llama-3.2-1B-General-Best	0.25	general , creative, uncensored

Heal pass (training)

• Method: QLoRA (4-bit NF4, LoRA r=16)
• Data: 200 rows sampled from teknium/OpenHermes-2.5
• Epochs: 3 · max_len: 1024 · grad_accum: 8 · packing on
• Loss: stable ~1.5 throughout (learning without overfitting the 200 rows)

Tests in Files

Assessment

The heal pass did its job: output is tighter, the poem lost its essay-style afterword, and answers end when they should. Reasoning ability is unchanged, just more confidently expressed, which cuts both ways (cleaner right answers, more assertive wrong ones). The 200-row pass tuned behavior without touching the merged capabilities underneath.

Safety: the heal pass did not restore refusal behavior. Still effectively uncensored, will attempt unsafe requests.