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Model: MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged Source: Original Platform
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README.md
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---
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license: llama3.2
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base_model: meta-llama/Llama-3.2-3B-Instruct
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- medical
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- healthcare
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- clinical
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- clinical-decision-support
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- question-answering
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- medical-qa
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- llama
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- llama-3.2
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- qlora
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- parameter-efficient-fine-tuning
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- merged
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datasets:
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- MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset
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- medalpaca/medical_meadow_medqa
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- medalpaca/medical_meadow_medical_flashcards
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- medalpaca/medical_meadow_wikidoc
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- medalpaca/medical_meadow_wikidoc_patient_information
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- medalpaca/medical_meadow_cord19
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- medalpaca/medical_meadow_pubmed_causal
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- openlifescienceai/medmcqa
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- bigbio/med_qa
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- qiaojin/PubMedQA
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- deepset/covid_qa_deepset
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---
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# Med-LLaMA3.2-3B — Medical (merged, standalone)
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> A full, ready-to-use medical model: **Llama-3.2-3B** adapted to the medical domain with **QLoRA**, with
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> the LoRA weights **merged back into the base**. Load it directly with `transformers` — no adapter, no
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> PEFT, no extra steps. For the lightweight LoRA-adapter version (apply on top of the base yourself), see
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> the link below.
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This is the **3B (balanced / mid-tier)** member of the **Med-LLaMA3** family introduced in the paper
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*“Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large
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Language Models”* (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical
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domain by training only a small fraction of the base model’s parameters (**5.70% for this 3B variant**),
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achieving strong medical question-answering performance while keeping the memory footprint low — enabling
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development and inference on low-cost, consumer-grade hardware.
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The 3B variant offers a **balanced trade-off between computational efficiency and capacity** — a
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competitive mid-tier option with meaningfully better accuracy than the 1B and lower compute than the 8B.
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- 📄 **Paper:** [Med-LLaMA3 (Applied Sciences 2026, 16(12), 6158)](https://www.mdpi.com/2076-3417/16/12/6158) · DOI: [10.3390/app16126158](https://doi.org/10.3390/app16126158)
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- 💻 **Code:** [github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers)
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- 🧩 **LoRA-adapter version:** [`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned)
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---
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## Model details
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| | |
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|---|---|
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| **This model** | Standalone, merged checkpoint (base + medical LoRA, fused) |
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| **Base model** | [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
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| **How it was made** | QLoRA fine-tuning (4-bit NF4 base + LoRA, `r=128`, `α=256`, all linear layers), then `merge_and_unload()` into the base |
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| **Trainable parameters (fine-tuning)** | 194.51 M = **5.70%** of the 3.40 B total (base frozen during training) |
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| **Released weights** | bfloat16 (full precision; not quantized) |
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| **Parameters** | ~3.21 B |
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| **Architecture** | 28 decoder layers · hidden size 3072 · intermediate size 8192 · GQA (24 query heads, 8 KV heads) |
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| **Context window** | 128K tokens |
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| **Vocabulary** | 128,256 tokens |
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| **Language** | English |
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| **License** | [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) |
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> **Adapter vs. merged.** This repo is the **merged** model — the medical LoRA is already fused into the
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> weights, so you load it like any standard causal-LM. If you instead want the small (~MB) adapter to
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> apply on top of `meta-llama/Llama-3.2-3B-Instruct` yourself, use the
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> [adapter repo](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned). Both
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> produce identical outputs.
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---
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## Intended uses
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**Primary use cases**
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- Medical **question answering** (multiple-choice and open-ended).
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- Clinical knowledge lookup and **clinical decision support** assistance.
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- A balanced mid-tier option when the 1B is too small and the 8B is too heavy.
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- A research baseline for parameter-efficient fine-tuning of small LLaMA models in healthcare.
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**Out of scope / not intended for**
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- Autonomous clinical decision-making or direct patient care without a qualified clinician in the loop.
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- Generating definitive diagnoses, prescriptions, or treatment plans.
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- Use as a substitute for professional medical advice, emergency services, or licensed care.
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See **[Limitations & responsible use](#limitations--responsible-use)** before any applied use.
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---
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## How to use
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This is a standalone model — load it directly, no adapter step required.
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```bash
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pip install -U transformers accelerate torch
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```
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### Quick start (`pipeline`)
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```python
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import torch
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from transformers import pipeline
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MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"
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pipe = pipeline("text-generation", model=MODEL, torch_dtype=torch.bfloat16, device_map="auto")
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messages = [
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{"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
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{"role": "user", "content": "What is the first-line treatment for uncomplicated community-acquired pneumonia in a healthy adult?"},
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]
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out = pipe(messages, max_new_tokens=256, do_sample=False)
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print(out[0]["generated_text"][-1]["content"])
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```
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### Full control (`AutoModelForCausalLM`)
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto")
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model.eval()
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messages = [
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{"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
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{"role": "user", "content": "Explain the mechanism of action of metformin."},
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(inputs, max_new_tokens=256, do_sample=False, temperature=0.0)
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print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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### Low-memory 4-bit inference
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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# pip install -U bitsandbytes
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MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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model = AutoModelForCausalLM.from_pretrained(MODEL, quantization_config=bnb_config, device_map="auto")
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```
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---
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## Training data
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The Med-LLaMA3 family was fine-tuned on a curated **medical instruction dataset of over 1.5 million
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samples**, organized along a three-axis taxonomy: **source type** (examination QA, clinical dialogue,
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biomedical literature, encyclopedic reference) × **clinical granularity** (basic science, clinical
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reasoning, patient communication) × **task format** (multiple-choice, open-ended QA, generative
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dialogue). All sources were consolidated into a unified instruction–response schema
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(`system`, `context`, `question`, `answer`, `choices`).
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Sources include:
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- **MedAlpaca / Medical Meadow** collection — MEDIQA, Medical Flashcards, WikiDoc, WikiDoc Patient
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Information, MedQA, CORD-19, and PubMed Causal subsets
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- **MedMCQA** — Indian medical entrance exam (AIIMS & NEET PG) multiple-choice questions
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- **MedQA-USMLE** — USMLE-style 4-option multiple-choice questions (English)
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- **BigBIO MedQA** — standardized biomedical QA
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- **PubMedQA** — research questions over PubMed abstracts (yes/no/maybe)
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- **COVID-QA (deepset)** — COVID-19 / SARS-CoV-2 question answering
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- **MedQuAD** — consumer-health QA compiled from authoritative NIH sources
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- **HealthCareMagic** — real-world patient–doctor conversation transcripts
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The data-cleaning and corpus-assembly scripts are released in the
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[code repository](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers), and the final compiled
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fine-tuning dataset is available at
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[`MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset`](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset).
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> **Evaluation integrity:** The eight **MMLU medical subsets** were used **only for held-out
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> evaluation** and were **excluded** from the fine-tuning corpus. For benchmarks with official splits
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> (MedMCQA, MedQA-USMLE, PubMedQA), only the official **training** partitions were used for fine-tuning.
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---
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## Training procedure
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This model was produced by QLoRA fine-tuning followed by merging the adapter into the base. LoRA and
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optimization settings are identical across the 1B, 3B, and 8B variants; sequence length, batch size, and
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gradient accumulation are scaled to each model’s memory footprint. The settings below are for the **3B**
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variant.
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| Setting | Value (3B) |
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|---|---|
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| Method | QLoRA (4-bit NF4 base, LoRA adapters in higher precision) → merged into base |
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| LoRA `r` / `α` / dropout / bias | 128 / 256 / 0.05 / none |
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| Target modules | All linear layers (q, k, v, o, gate, up, down) |
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| Trainable params | 194.51 M (5.70% of 3.40 B) |
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| Quantization (training) | 4-bit NF4 with double quantization (bitsandbytes) |
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| Optimizer | Paged AdamW 8-bit (β₁ = 0.9, β₂ = 0.999), weight decay 0.1 |
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| Learning rate / schedule | 2.0 × 10⁻⁵ / cosine annealing, 5 warmup steps |
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| Epochs | 5 |
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| Max sequence length | 2048 |
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| Batch size / grad accumulation | 4 per device / 16 steps |
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| Max gradient norm | 1.0 |
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| Precision & memory | bfloat16 · gradient checkpointing · DeepSpeed ZeRO-2 · FlashAttention-2 |
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| Hardware | 2 × NVIDIA RTX 4050 (12 GB), ~30 days |
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| Experiment tracking | Weights & Biases |
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---
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## Evaluation
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Evaluation in the paper uses the **EleutherAI LM Evaluation Harness** with **5-shot** prompting on the
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eight MMLU medical subsets (Anatomy, Clinical Knowledge, College Biology, College Medicine, Medical
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Genetics, Nutrition, Professional Medicine, Virology). Reported comparisons include **McNemar’s test**
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p-values and **95% bootstrap confidence intervals**.
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The table below reports the 3B model’s 5-shot accuracy (%) on each MMLU medical subset, with 95%
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bootstrap confidence intervals (1000 resamples), as published in Table 7 of the paper. For context, the
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family’s mean accuracy scales with model size: **1B = 48.64%**, **3B = 64.24%**, **8B = 75.71%**.
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| MMLU medical subset (5-shot) | Med-LLaMA3.2-3B (acc. %) |
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|---|---|
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| Anatomy | 59.52 (±4.26) |
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| Clinical Knowledge | 68.17 (±2.89) |
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| College Biology | 71.53 (±3.77) |
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| College Medicine | 57.65 (±3.78) |
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| Medical Genetics | 75.00 (±4.35) |
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| Nutrition | 68.32 (±2.69) |
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| Professional Medicine | 70.59 (±2.77) |
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| Virology | 43.17 (±3.84) |
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| **Mean (8 subsets)** | **64.24** |
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> The merged model is functionally identical to the base + adapter, so these scores apply to both. The
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> paper reports an untuned baseline only for the 8B model (vs. `Llama-3.1-8B-Instruct`); it does **not**
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> include an untuned `Llama-3.2-3B` baseline on these subsets. See Table 7 of the paper for the full
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> cross-model comparison (1B, 8B, and other ≤8B models) with statistical tests.
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See the [paper](https://www.mdpi.com/2076-3417/16/12/6158) for full tables, statistical tests, and
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confidence intervals.
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---
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## Limitations & responsible use
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- **Not a medical device.** This model is a research artifact. It must **not** be used for autonomous
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diagnosis, treatment, prescribing, or any decision affecting patient care without review by a
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qualified healthcare professional.
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- **Hallucination risk.** Like all LLMs, it can produce fluent but incorrect or fabricated medical
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information. Always verify outputs against authoritative sources.
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- **Mid-tier capacity.** The 3B is a balanced variant; for the highest accuracy on complex clinical
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reasoning, prefer the 8B variant when resources allow. For the smallest footprint, the 1B is available.
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- **Abbreviation ambiguity.** Medical abbreviations are a known error source. The paper’s safety pilot
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shows that **context-disambiguation preprocessing** reduces the highest-severity abbreviation
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errors (from 30% to 10% on a held-out set); consider applying similar preprocessing.
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- **Data & bias.** Training data may under-represent certain populations, conditions, or regional
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practices, and may encode biases present in the source corpora.
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- **Privacy & compliance.** Do not input protected health information (PHI) unless your deployment is
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appropriately secured and compliant with applicable regulations (e.g., HIPAA, GDPR).
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- **English only.** Performance outside English is not evaluated.
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---
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## License
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This model is released under the **[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)**,
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inherited from the base model. By using it you agree to Meta’s Llama 3.2 license terms and
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Acceptable Use Policy. Review the licenses of the individual training datasets for any additional
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restrictions on derived use.
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---
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## Citation
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If you use this model, please cite the paper:
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```bibtex
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@article{aboelenen2026medllama3,
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title = {Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models},
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author = {Abo El-Enen, Mohamed Ahmed and Ismail, Sally S. and Nazmy, Taymoor Mohamed},
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journal = {Applied Sciences},
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volume = {16},
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number = {12},
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pages = {6158},
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year = {2026},
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publisher = {MDPI},
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doi = {10.3390/app16126158},
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url = {https://www.mdpi.com/2076-3417/16/12/6158}
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}
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```
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## Authors & contact
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Mohamed Ahmed Abo El-Enen, Sally S. Ismail, and Taymoor Mohamed Nazmy
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Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
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---
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## Model family
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| Variant | Type | Repository |
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|---|---|---|
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| Med-LLaMA3.2-1B | Adapter | [`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned) |
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| Med-LLaMA3.2-1B | Merged | [`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned-merged) |
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| Med-LLaMA3.2-3B | Adapter | [`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned) |
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| **Med-LLaMA3.2-3B** | **Merged** | **this repo** — [`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged) |
|
||||
| Med-LLaMA3.1-8B | Adapter | [`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned) |
|
||||
| Med-LLaMA3.1-8B | Merged | [`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged) |
|
||||
|
||||
**Fine-tuning dataset:** [`MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset`](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset)
|
||||
93
chat_template.jinja
Normal file
93
chat_template.jinja
Normal file
@@ -0,0 +1,93 @@
|
||||
{{- bos_token }}
|
||||
{%- if custom_tools is defined %}
|
||||
{%- set tools = custom_tools %}
|
||||
{%- endif %}
|
||||
{%- if not tools_in_user_message is defined %}
|
||||
{%- set tools_in_user_message = true %}
|
||||
{%- endif %}
|
||||
{%- if not date_string is defined %}
|
||||
{%- if strftime_now is defined %}
|
||||
{%- set date_string = strftime_now("%d %b %Y") %}
|
||||
{%- else %}
|
||||
{%- set date_string = "26 Jul 2024" %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if not tools is defined %}
|
||||
{%- set tools = none %}
|
||||
{%- endif %}
|
||||
|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set system_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set system_message = "" %}
|
||||
{%- endif %}
|
||||
|
||||
{#- System message #}
|
||||
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if tools is not none %}
|
||||
{{- "Environment: ipython\n" }}
|
||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n" }}
|
||||
{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
39
config.json
Normal file
39
config.json
Normal file
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 24,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 32.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.57.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.1"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:13cbd6d16e927a0c5bad54102514e6e18b4a47b3a6eb911e39d678d328d19f55
|
||||
size 4965799096
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:7b770216613ac5c34d7c54bdff1fa616bc4e338a9d0b20af6303e48c295ee23c
|
||||
size 1459729952
|
||||
262
model.safetensors.index.json
Normal file
262
model.safetensors.index.json
Normal file
@@ -0,0 +1,262 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 3212749824,
|
||||
"total_size": 6425499648
|
||||
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|
||||
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2065
tokenizer_config.json
Normal file
2065
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user