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Model: Wothmag07/counseLLM Source: Original Platform
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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tags:
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- mental-health
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- counseling
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- llama
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- qlora
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- dpo
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- sft
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- alignment
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base_model: meta-llama/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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---
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# CounseLLM — Empathy-Aligned Conversational Support LLM
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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.
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> **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](https://988lifeline.org/) (call or text 988) or your local emergency services.
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## Model Details
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- **Developed by:** [Gowtham Arulmozhii](https://github.com/wothmag07)
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- **Model type:** Causal Language Model (text generation)
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- **Language:** English
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- **License:** Apache 2.0
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- **Base model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
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- **Repository:** [GitHub](https://github.com/wothmag07/counseLLM)
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## Training
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### Two-Stage Alignment Pipeline
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**Stage 1 — Supervised Fine-Tuning (SFT)**
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| Parameter | Value |
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|---|---|
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| Method | QLoRA (4-bit NF4 + double quantization) |
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| LoRA Rank / Alpha | 64 / 128 |
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| Learning Rate | 2e-4 (cosine scheduler) |
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| Epochs | 2 |
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| Effective Batch Size | 16 |
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| Training Data | 36K multi-source counseling examples |
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| GPU | NVIDIA H100 80GB |
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| Training Time | ~3 hours |
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**Stage 2 — Direct Preference Optimization (DPO)**
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| Parameter | Value |
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|---|---|
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| Method | QLoRA on SFT-merged base |
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| LoRA Rank / Alpha | 16 / 32 |
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| Beta (KL penalty) | 0.5 |
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| Learning Rate | 1e-5 (cosine scheduler) |
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| Epochs | 1 |
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| Effective Batch Size | 8 |
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| Training Data | ~2K preference-filtered pairs |
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| GPU | NVIDIA H100 80GB |
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| Training Time | ~30 minutes |
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### Training Data
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**SFT (36K examples from 5 sources)**
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| Source | Examples | Type |
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|---|---|---|
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| [MentalChat16K](https://huggingface.co/datasets/ShenLab/MentalChat16K) | ~16K | Synthetic + clinical |
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| [empathetic_dialogues](https://huggingface.co/datasets/Estwld/empathetic_dialogues_llm) | ~10K | Real human multi-turn |
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| [Psych8k](https://huggingface.co/datasets/EmoCareAI/Psych8k) | ~8K | Real therapist transcripts |
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| [counsel-chat](https://huggingface.co/datasets/nbertagnolli/counsel-chat) | ~940 | Real therapist Q&A |
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| [ESConv](https://huggingface.co/datasets/thu-coai/esconv) | ~910 | Real human + strategy labels |
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**DPO (~2K preference pairs)**
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| Source | Pairs | Selection |
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|---|---|---|
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| [PsychoCounsel-Preference](https://huggingface.co/datasets/Psychotherapy-LLM/PsychoCounsel-Preference) | ~2K | Rating-gap filtered across 7 dimensions |
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## Evaluation
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### Automated Metrics
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| Metric | Base | SFT | DPO |
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|---|---|---|---|
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| Perplexity | 4.18 | 3.64 | **3.13** |
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| BERTScore F1 | 0.8598 | 0.8527 | 0.8492 |
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| ROUGE-L F1 | 0.1065 | 0.0772 | 0.0790 |
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| Distinct-1 | 0.273 | **0.331** | 0.262 |
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| Distinct-2 | 0.658 | **0.807** | 0.712 |
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| Avg Response Length | 98 | 119 | **198** |
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### LLM-as-Judge (GPT-4o, 1-5 scale)
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| Dimension | Base | SFT | DPO |
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|---|---|---|---|
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| Empathy | 4.40 | 3.48 | **4.88** |
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| Safety | 4.28 | 3.84 | **4.60** |
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| Relevance | 4.68 | 3.72 | **4.88** |
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| Helpfulness | 4.04 | 3.04 | **4.48** |
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| **Overall** | 4.35 | 3.52 | **4.71** |
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Evaluated on 25 curated prompts across 18 mental health categories (anxiety, depression, grief, crisis, relationships, trauma, etc.).
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Wothmag07/counseLLM"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"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."},
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{"role": "user", "content": "I've been feeling really anxious about work lately and I can't sleep."},
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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## Uses
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### Intended Use
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- Research and educational purposes in AI-assisted mental health support
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- Studying alignment techniques (SFT + DPO) applied to sensitive domains
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- Demonstrating empathy-aligned language model fine-tuning
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### Out-of-Scope Use
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- **Clinical deployment** — this model is not validated for clinical use
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- **Crisis intervention** — should not be relied upon for suicide prevention or emergency situations
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- **Replacement for therapy** — not a substitute for licensed mental health professionals
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## Bias, Risks, and Limitations
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- The model may reflect biases present in training data (both real and synthetic sources)
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- Responses may sometimes be generic or miss nuances of specific cultural contexts
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- The model may generate plausible-sounding but clinically inaccurate advice
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- Training data is predominantly English and may not generalize to other languages
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- Should not be deployed in production clinical settings without extensive safety review
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## Environmental Impact
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- **Hardware:** NVIDIA H100 80GB
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- **Training Time:** ~3.5 hours total (SFT: 3h, DPO: 30min)
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- **Cloud Provider:** [Modal](https://modal.com/)
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## Tech Stack
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| Component | Technology |
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|---|---|
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| Base Model | Meta Llama 3.1 8B Instruct |
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| Training | HuggingFace TRL (SFTTrainer, DPOTrainer) |
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| Quantization | QLoRA via bitsandbytes (4-bit NF4) |
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| Adapters | PEFT (LoRA) |
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| Infrastructure | Modal (H100 GPUs) |
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| Experiment Tracking | Weights & Biases |
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| Evaluation | BERTScore, ROUGE-L, GPT-4o Judge |
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## Citation
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```bibtex
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@misc{counseLLM2026,
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author = {Gowtham Arulmozhii},
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title = {CounseLLM: Empathy-Aligned Conversational Support LLM},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/Wothmag07/counseLLM}
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}
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```
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## Model Card Contact
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- GitHub: [@wothmag07](https://github.com/wothmag07)
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config.json
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"_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"use_cache": true,
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"vocab_size": 128256
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}
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|
||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user