counseLLM/README.md

library_name, license, language, tags, base_model, pipeline_tag

library_name	license	language	tags	base_model	pipeline_tag
transformers	apache-2.0	en	mental-health counseling llama qlora dpo sft alignment	meta-llama/Llama-3.1-8B-Instruct	text-generation

CounseLLM — Empathy-Aligned Conversational Support LLM

An empathy-aligned conversational support model fine-tuned from Llama 3.1 8B Instruct using a two-stage alignment pipeline: Supervised Fine-Tuning (SFT) on 36K counseling examples followed by Direct Preference Optimization (DPO) on ~2K preference-filtered pairs.

Disclaimer: This is an AI research project and is not a substitute for professional mental health care. If you are in crisis, please contact the 988 Suicide & Crisis Lifeline (call or text 988) or your local emergency services.

Model Details

• Developed by: Gowtham Arulmozhii
• Model type: Causal Language Model (text generation)
• Language: English
• License: Apache 2.0
• Base model: meta-llama/Llama-3.1-8B-Instruct
• Repository: GitHub

Training

Two-Stage Alignment Pipeline

Stage 1 — Supervised Fine-Tuning (SFT)

Parameter	Value
Method	QLoRA (4-bit NF4 + double quantization)
LoRA Rank / Alpha	64 / 128
Learning Rate	2e-4 (cosine scheduler)
Epochs	2
Effective Batch Size	16
Training Data	36K multi-source counseling examples
GPU	NVIDIA H100 80GB
Training Time	~3 hours

Stage 2 — Direct Preference Optimization (DPO)

Parameter	Value
Method	QLoRA on SFT-merged base
LoRA Rank / Alpha	16 / 32
Beta (KL penalty)	0.5
Learning Rate	1e-5 (cosine scheduler)
Epochs	1
Effective Batch Size	8
Training Data	~2K preference-filtered pairs
GPU	NVIDIA H100 80GB
Training Time	~30 minutes

Training Data

SFT (36K examples from 5 sources)

Source	Examples	Type
MentalChat16K	~16K	Synthetic + clinical
empathetic_dialogues	~10K	Real human multi-turn
Psych8k	~8K	Real therapist transcripts
counsel-chat	~940	Real therapist Q&A
ESConv	~910	Real human + strategy labels

DPO (~2K preference pairs)

Source	Pairs	Selection
PsychoCounsel-Preference	~2K	Rating-gap filtered across 7 dimensions

Evaluation

Automated Metrics

Metric	Base	SFT	DPO
Perplexity	4.18	3.64	3.13
BERTScore F1	0.8598	0.8527	0.8492
ROUGE-L F1	0.1065	0.0772	0.0790
Distinct-1	0.273	0.331	0.262
Distinct-2	0.658	0.807	0.712
Avg Response Length	98	119	198

LLM-as-Judge (GPT-4o, 1-5 scale)

Dimension	Base	SFT	DPO
Empathy	4.40	3.48	4.88
Safety	4.28	3.84	4.60
Relevance	4.68	3.72	4.88
Helpfulness	4.04	3.04	4.48
Overall	4.35	3.52	4.71

Evaluated on 25 curated prompts across 18 mental health categories (anxiety, depression, grief, crisis, relationships, trauma, etc.).

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Wothmag07/counseLLM"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a mental health counselor providing supportive, empathetic guidance. Respond by first acknowledging the person's feelings, then explore their situation with open-ended questions. Use techniques like reflective listening, validation, and gentle reframing. Keep responses warm, conversational, and non-judgmental."},
    {"role": "user", "content": "I've been feeling really anxious about work lately and I can't sleep."},
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.1,
)
response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Uses

Intended Use

• Research and educational purposes in AI-assisted mental health support
• Studying alignment techniques (SFT + DPO) applied to sensitive domains
• Demonstrating empathy-aligned language model fine-tuning

Out-of-Scope Use

• Clinical deployment — this model is not validated for clinical use
• Crisis intervention — should not be relied upon for suicide prevention or emergency situations
• Replacement for therapy — not a substitute for licensed mental health professionals

Bias, Risks, and Limitations

• The model may reflect biases present in training data (both real and synthetic sources)
• Responses may sometimes be generic or miss nuances of specific cultural contexts
• The model may generate plausible-sounding but clinically inaccurate advice
• Training data is predominantly English and may not generalize to other languages
• Should not be deployed in production clinical settings without extensive safety review

Environmental Impact

• Hardware: NVIDIA H100 80GB
• Training Time: ~3.5 hours total (SFT: 3h, DPO: 30min)
• Cloud Provider: Modal

Tech Stack

Component	Technology
Base Model	Meta Llama 3.1 8B Instruct
Training	HuggingFace TRL (SFTTrainer, DPOTrainer)
Quantization	QLoRA via bitsandbytes (4-bit NF4)
Adapters	PEFT (LoRA)
Infrastructure	Modal (H100 GPUs)
Experiment Tracking	Weights & Biases
Evaluation	BERTScore, ROUGE-L, GPT-4o Judge

Citation

@misc{counseLLM2026,
  author = {Gowtham Arulmozhii},
  title = {CounseLLM: Empathy-Aligned Conversational Support LLM},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Wothmag07/counseLLM}
}

Model Card Contact

• GitHub: @wothmag07