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Model: somrajmondal/phi3-mini-finance-lora-fp16 Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- phi3
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- finance
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- lora
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- qlora
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- finetuned
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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# 💹 Phi-3-mini Finance LoRA (fp16)
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A domain-specialized version of Microsoft's **Phi-3-mini-4k-instruct**, fine-tuned on financial Q&A data using **LoRA + 4-bit NF4 quantization** — trained entirely on a free Google Colab T4 GPU.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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## 📋 Model Details
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| Field | Details |
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|---|---|
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| **Developed by** | [somrajmondal](https://huggingface.co/somrajmondal) |
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| **Base model** | unsloth/phi-3-mini-4k-instruct-bnb-4bit |
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| **Model type** | Causal Language Model (Phi-3 architecture) |
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| **Parameters** | 3.8B total / 29.8M trainable (0.78%) |
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| **Language** | English |
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| **License** | Apache 2.0 |
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| **Fine-tuning method** | LoRA (Low-Rank Adaptation) |
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| **Quantization** | 4-bit NF4 during training, merged to fp16 |
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---
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## 🎯 What This Model Does
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This model is fine-tuned to answer **finance and investment questions** clearly and accurately. It was trained on the `gbharti/finance-alpaca` dataset covering topics like:
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- Stock market concepts (P/E ratio, dividends, market cap)
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- Investment strategies (ETFs, mutual funds, dollar-cost averaging)
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- Fixed income (bonds, yields, interest rates)
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- Personal finance (compound interest, savings, budgeting)
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- Financial planning and portfolio diversification
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---
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## 🏋️ Training Details
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| Setting | Value |
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|---|---|
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| **Dataset** | [gbharti/finance-alpaca](https://huggingface.co/datasets/gbharti/finance-alpaca) |
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| **Training rows** | 5,000 |
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| **Epochs** | 2 |
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| **Total steps** | 1,250 |
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| **Batch size** | 2 (effective: 8 with grad accumulation) |
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| **Learning rate** | 2e-4 (cosine scheduler) |
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| **Optimizer** | AdamW 8-bit |
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| **LoRA rank (r)** | 16 |
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| **LoRA alpha** | 16 |
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| **LoRA dropout** | 0.05 |
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| **LoRA target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Max sequence length** | 1024 |
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| **Final training loss** | 2.18 |
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| **Peak VRAM used** | 3.69 GB |
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| **Training hardware** | Google Colab Free T4 (15GB VRAM) |
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| **Training time** | ~2 hours 20 minutes |
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| **Framework** | Unsloth + HuggingFace TRL |
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---
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## 🚀 How to Use
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### Quick Start (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "somrajmondal/phi3-mini-finance-lora-fp16"
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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.float16,
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device_map="auto",
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)
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question = "What is compound interest and why is it important?"
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prompt = f"""<|user|>
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{question}
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<|end|>
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<|assistant|>
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=300,
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do_sample=False,
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repetition_penalty=1.3,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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### With Unsloth (Faster Inference)
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "somrajmondal/phi3-mini-finance-lora-fp16",
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max_seq_length = 1024,
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dtype = None,
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load_in_4bit = True, # set False for full fp16
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)
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FastLanguageModel.for_inference(model)
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question = "What is the difference between a stock and a bond?"
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prompt = f"""<|user|>
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{question}
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<|end|>
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<|assistant|>
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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**inputs,
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max_new_tokens=300,
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do_sample=False,
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repetition_penalty=1.3,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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---
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## 💬 Prompt Format
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This model uses the **Phi-3 chat template**. Always wrap your input like this:
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```
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<|user|>
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Your finance question here
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<|end|>
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<|assistant|>
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```
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---
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## 📊 Example Outputs
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**Q: What is a P/E ratio?**
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> The price-to-earnings (P/E) ratio measures a company's current share price relative to its earnings per share. A high P/E suggests investors expect future growth, while a low P/E may indicate an undervalued stock or slower expected growth.
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**Q: What is dollar cost averaging?**
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> Dollar cost averaging is an investment strategy where you invest a fixed amount of money at regular intervals, regardless of market conditions. This reduces the impact of volatility and removes the need to time the market.
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---
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## ⚠️ Limitations
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- Trained on only 5,000 rows — may lack depth on niche financial topics
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- Not suitable for real financial advice — always consult a professional
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- May occasionally produce incomplete answers on complex multi-part questions
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- Training loss of 2.18 indicates room for improvement with more epochs/data
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---
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## 🔧 Recommended Inference Settings
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```python
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# For factual / accurate answers (recommended)
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do_sample = False
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repetition_penalty = 1.3
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max_new_tokens = 300
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# For more creative / detailed answers
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do_sample = True
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temperature = 0.3
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top_p = 0.9
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```
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---
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## 📦 Training Framework
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- [Unsloth](https://github.com/unslothai/unsloth) — 2x faster training, 60% less VRAM
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- [HuggingFace TRL](https://github.com/huggingface/trl) — SFTTrainer
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- [PEFT](https://github.com/huggingface/peft) — LoRA adapters
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- [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) — 4-bit NF4 quantization
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---
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## 📄 Citation
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If you use this model, please cite the base model and dataset:
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```bibtex
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@misc{phi3-mini-finance-lora,
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author = {somrajmondal},
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title = {Phi-3-mini Finance LoRA},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/somrajmondal/phi3-mini-finance-lora-fp16}
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}
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```
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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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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"torch_dtype": "float16",
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"eos_token_id": 32000,
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"head_dim": 96,
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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": "mistral",
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": 2048,
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"tie_word_embeddings": false,
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"unsloth_version": "2026.5.2",
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"use_cache": false,
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"vocab_size": 32064
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}
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"model.layers.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
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||||
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
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||||
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
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||||
"model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
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||||
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
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|
||||
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
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||||
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||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
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|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
277210
tokenizer.json
Normal file
277210
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
131
tokenizer_config.json
Normal file
131
tokenizer_config.json
Normal file
@@ -0,0 +1,131 @@
|
||||
{
|
||||
"add_prefix_space": null,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"is_local": false,
|
||||
"legacy": false,
|
||||
"model_max_length": 4096,
|
||||
"pad_token": "<|placeholder6|>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"32000": {
|
||||
"content": "<|endoftext|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
"content": "<|assistant|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32002": {
|
||||
"content": "<|placeholder1|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32003": {
|
||||
"content": "<|placeholder2|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32004": {
|
||||
"content": "<|placeholder3|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32005": {
|
||||
"content": "<|placeholder4|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32006": {
|
||||
"content": "<|system|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32007": {
|
||||
"content": "<|end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32008": {
|
||||
"content": "<|placeholder5|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32009": {
|
||||
"content": "<|placeholder6|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"32010": {
|
||||
"content": "<|user|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": true,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user