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Model: PhantomAjusshi/phi3-auditor-merged Source: Original Platform
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README.md
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---
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license: mit
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language:
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- en
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base_model:
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- microsoft/Phi-3-mini-4k-instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- phi3
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- lora
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- peft
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- clinical-ai
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- model-audit
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- text-generation
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- fine-tuned
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- healthcare
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- safetensors
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---
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# 🏥 phi3-auditor-merged
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**Phi-3-mini fine-tuned for clinical AI model auditing.**
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This model takes a JSON object of ML performance metrics (AUC, ECE, drift, label shift, etc.) and returns a structured health classification label plus a detailed explanation — helping teams audit deployed clinical models for drift, calibration failure, class imbalance, and other issues.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base Model** | [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |
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| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) via PEFT |
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| **Training Precision** | 8-bit quantized (BitsAndBytesConfig) |
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| **Merged Precision** | FP16 (float16 safetensors) |
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| **Parameters** | ~3.8B |
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| **Model Size** | 7.65 GB (2 safetensor shards) |
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| **LoRA Rank (r)** | 16 |
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| **LoRA Alpha** | 32 |
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| **LoRA Dropout** | 0.05 |
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| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
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| **Task Type** | Causal Language Modeling |
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| **PEFT Version** | 0.18.0 |
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| **Training Epochs** | 3 |
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| **Final Loss** | ~0.41 |
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---
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## Intended Use
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### What this model does
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Given a JSON report of clinical ML model performance metrics, the model:
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1. Assigns a **Category** label (e.g. `Calibration Failure`, `Major Drift`, `Class Imbalance Problem`, `Healthy`)
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2. Generates a concise **Explanation** with observations and recommendations
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### Intended users
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- ML engineers monitoring deployed clinical models
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- Healthcare data science teams running periodic model audits
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- Researchers studying automated model health assessment
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### Out-of-scope use
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- Not suitable for direct clinical decision-making or patient diagnosis
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- Not a replacement for domain expert review of model performance
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- Not designed for non-clinical ML tasks
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- Should not be used on data types outside its training distribution (non-tabular metrics, images, etc.)
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---
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## How to Use
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### Requirements
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```bash
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pip install transformers torch accelerate
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```
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### Basic inference
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "PhantomAjusshi/phi3-auditor-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto",
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trust_remote_code=True, # Required for custom Phi-3 modeling files
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)
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report = """{
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"auc": 0.863,
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"accuracy": 0.83,
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"precision": 0.79,
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"recall": 0.69,
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"f1": 0.79,
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"ece": 0.278,
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"brier": 0.263,
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"drift": 0.03,
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"missing_rate": 0.003,
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"label_shift": 0.06,
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"pos_rate": 0.10,
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"data_integrity_issues": 0
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}"""
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prompt = (
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f"<|system|>\nYou are a clinical AI auditor model.\n"
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f"<|user|>\nInstruction: Analyze the clinical model report and classify its health.\n\nReport:\n{report}\n"
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f"<|assistant|>\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.inference_mode():
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outputs = model.generate(
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**inputs,
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max_new_tokens=400,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.2,
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do_sample=True,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Extract only the assistant's reply
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reply = response.split("<|assistant|>")[-1].strip()
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print(reply)
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```
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### Expected output format
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```
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Category: Calibration Failure
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Explanation: High calibration error (ECE 0.278) despite reasonable discrimination (AUC 0.863).
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The model's probability outputs are poorly aligned with actual outcomes. Recommend
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recalibration using Platt scaling or isotonic regression, and threshold review.
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```
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### Input metrics reference
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| Metric | Description |
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|---|---|
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| `auc` | Area Under the ROC Curve |
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| `accuracy` | Overall classification accuracy |
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| `precision` | Positive predictive value |
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| `recall` | Sensitivity / true positive rate |
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| `f1` | Harmonic mean of precision and recall |
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| `ece` | Expected Calibration Error |
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| `brier` | Brier score (probabilistic accuracy) |
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| `drift` | Feature distribution drift score |
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| `missing_rate` | Rate of missing input features |
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| `label_shift` | Output label distribution shift |
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| `pos_rate` | Positive prediction rate |
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| `data_integrity_issues` | Count of detected data quality issues |
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---
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## Training Details
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### Dataset
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- **Name:** Custom synthetic clinical audit dataset (`audit_dataset_v2_5000.json`)
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- **Size:** 5,000 labeled samples
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- **Split:** 80% train (4,000) / 20% test (1,000)
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- **Format:** JSONL — each record has `instruction`, `input` (metrics JSON), `output` (category + explanation)
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- **Generation date:** November 17, 2025
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Each sample pairs a set of synthetic model performance metrics with a human-written audit label and explanation covering categories such as:
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- Healthy / Passing
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- Calibration Failure
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- Major Drift / Potential Drift
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- Class Imbalance Problem
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- Data Integrity Issue
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- Needs Review / Critical Failure
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### Training procedure
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The base model was loaded in 8-bit using `BitsAndBytesConfig` and adapted with LoRA targeting the attention projection layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`). After training, the LoRA adapter was merged into the base model weights using `peft.merge_and_unload()` and saved as full FP16 safetensors.
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**Prompt format used during training:**
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```
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<|system|>
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You are an AI auditor analyzing clinical model performance reports.
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<|user|>
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Instruction: Analyze the clinical model report and classify its health.
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Report:
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{ ...metrics JSON... }
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<|assistant|>
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Category: <label>
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Explanation: <explanation>
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```
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Epochs | 3 |
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| Batch size | 4 |
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| Gradient accumulation steps | 4 |
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| Effective batch size | 16 |
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| Learning rate | 1e-4 |
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| Warmup ratio | 0.1 |
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| Max sequence length | 512 |
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| Optimizer | AdamW (default) |
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| Precision | FP16 (mixed) |
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### Training loss
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| Step | Epoch | Loss |
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|---|---|---|
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| 50 | 0.22 | 1.623 |
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| 100 | 0.44 | 0.657 |
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| 150 | 0.67 | 0.444 |
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| 200 | 0.89 | 0.420 |
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| 300 | 1.33 | 0.413 |
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| 450 | 2.00 | 0.412 |
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| 600 | 2.67 | 0.408 |
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| 675 | 3.00 | ~0.410 |
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Loss converged rapidly after the first 150 steps, stabilizing around 0.41 for the remainder of training.
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---
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## Evaluation
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The model was evaluated on a held-out test set of 1,000 samples using weighted precision, recall, F1, and accuracy computed by extracting the `Category:` field from generated outputs and comparing to ground-truth labels.
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> Formal evaluation metrics will be added here once a full benchmark run is completed.
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---
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## Limitations & Bias
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- **Synthetic training data:** The model was trained entirely on synthetically generated audit reports. Real-world clinical model metrics may follow different distributions or contain edge cases not represented in training.
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- **Label sensitivity:** The model may be sensitive to metric combinations near decision boundaries between categories.
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- **No temporal reasoning:** The model does not reason about metric trends over time — each inference is based on a single snapshot of metrics.
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- **English only:** All training data is in English.
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- **Not a substitute for expert review:** Outputs should be treated as decision-support, not a final audit verdict.
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---
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## Repository & Related Work
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- **Training code:** [Hospital-Audit-Trained-Model (GitHub)](https://github.com/PhantomAjusshi/Hospital-Audit-Trained-Model)
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- **Web application:** [Hospital-Model-Audit-Website (GitHub)](https://github.com/PhantomAjusshi/Hospital-Model-Audit-Website) — a full-stack Next.js + FastAPI interface that uses this model via llama.cpp
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---
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## Citation
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If you use this model in your work, please cite:
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```bibtex
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@misc{phi3-auditor-merged,
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author = {PhantomAjusshi},
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title = {phi3-auditor-merged: Phi-3-mini fine-tuned for clinical AI model auditing},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/PhantomAjusshi/phi3-auditor-merged}
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}
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```
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---
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## License
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This model is released under the **MIT License**.
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The base model ([microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)) is subject to Microsoft's Phi-3 license. Please review it before use in commercial or production settings.
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added_tokens.json
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added_tokens.json
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{
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"<|assistant|>": 32001,
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"<|endoftext|>": 32000,
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"<|end|>": 32007,
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"<|placeholder1|>": 32002,
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"<|placeholder2|>": 32003,
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"<|placeholder3|>": 32004,
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"<|placeholder4|>": 32005,
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"<|placeholder5|>": 32008,
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"<|placeholder6|>": 32009,
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"<|system|>": 32006,
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"<|user|>": 32010
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}
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chat_template.jinja
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chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>
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' + message['content'] + '<|end|>
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'}}{% elif message['role'] == 'user' %}{{'<|user|>
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' + message['content'] + '<|end|>
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'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
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' + message['content'] + '<|end|>
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'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
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' }}{% else %}{{ eos_token }}{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi3.Phi3Config",
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"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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},
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"bos_token_id": 1,
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"dtype": "float16",
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"embd_pdrop": 0.0,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 4096,
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"model_type": "phi3",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"original_max_position_embeddings": 4096,
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"pad_token_id": 32000,
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"partial_rotary_factor": 1.0,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"sliding_window": 2047,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"use_cache": true,
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"vocab_size": 32064
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}
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configuration_phi3.py
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configuration_phi3.py
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# coding=utf-8
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
|
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
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||||
|
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""" Phi-3 model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
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"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
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}
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class Phi3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the
|
||||
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32064):
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Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
|
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`inputs_ids` passed when calling [`Phi3Model`].
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
||||
resid_pdrop (`float`, *optional*, defaults to 0.0):
|
||||
Dropout probability for mlp outputs.
|
||||
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the embeddings.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio after computing the attention scores.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model was trained with. This is used to determine the size of the
|
||||
original RoPE embeddings when using long scaling.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
The epsilon value used for the RMSNorm.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`dict`, *optional*):
|
||||
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
|
||||
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
|
||||
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
|
||||
divided by the number of attention heads divided by 2.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
The id of the "beginning-of-sequence" token.
|
||||
eos_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the "end-of-sequence" token.
|
||||
pad_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the padding token.
|
||||
sliding_window (`int`, *optional*):
|
||||
Sliding window attention window size. If `None`, no sliding window is applied.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import Phi3Model, Phi3Config
|
||||
|
||||
>>> # Initializing a Phi-3 style configuration
|
||||
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
|
||||
|
||||
>>> # Initializing a model from the configuration
|
||||
>>> model = Phi3Model(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "phi3"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32064,
|
||||
hidden_size=3072,
|
||||
intermediate_size=8192,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
resid_pdrop=0.0,
|
||||
embd_pdrop=0.0,
|
||||
attention_dropout=0.0,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=4096,
|
||||
original_max_position_embeddings=4096,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-5,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=32000,
|
||||
pad_token_id=32000,
|
||||
sliding_window=None,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attention_dropout = attention_dropout
|
||||
self.hidden_act = hidden_act
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.original_max_position_embeddings = original_max_position_embeddings
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self._rope_scaling_adjustment()
|
||||
self._rope_scaling_validation()
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
pad_token_id=pad_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _rope_scaling_adjustment(self):
|
||||
"""
|
||||
Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
|
||||
# For backward compatibility if previous version used "su" or "yarn"
|
||||
if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
|
||||
self.rope_scaling["type"] = "longrope"
|
||||
|
||||
def _rope_scaling_validation(self):
|
||||
"""
|
||||
Validate the `rope_scaling` configuration.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
|
||||
raise ValueError(
|
||||
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
|
||||
f"got {self.rope_scaling}"
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
|
||||
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
|
||||
if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
|
||||
raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
|
||||
if not (
|
||||
isinstance(rope_scaling_short_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
|
||||
)
|
||||
if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
|
||||
)
|
||||
if not (
|
||||
isinstance(rope_scaling_long_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
|
||||
)
|
||||
if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
|
||||
)
|
||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": [
|
||||
32000,
|
||||
32001,
|
||||
32007
|
||||
],
|
||||
"pad_token_id": 32000,
|
||||
"transformers_version": "4.57.3"
|
||||
}
|
||||
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
|
||||
oid sha256:d43804bb86e51b854b38ac96ce0518569ed47180af3a3aefa6f2c1a6137093d1
|
||||
size 4972489200
|
||||
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
|
||||
oid sha256:391164c58f36d91c24d7d29878069aa7a9c2d9829379efca16e1ef9e57585253
|
||||
size 2669692488
|
||||
203
model.safetensors.index.json
Normal file
203
model.safetensors.index.json
Normal file
@@ -0,0 +1,203 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 3821079552,
|
||||
"total_size": 7642159104
|
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},
|
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"weight_map": {
|
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||||
1563
modeling_phi3.py
Normal file
1563
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
}
|
||||
}
|
||||
277210
tokenizer.json
Normal file
277210
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
131
tokenizer_config.json
Normal file
131
tokenizer_config.json
Normal file
@@ -0,0 +1,131 @@
|
||||
{
|
||||
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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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|
||||
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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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|
||||
}
|
||||
Reference in New Issue
Block a user