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Model: integration1857/prescription-simplifier-mistral7b Source: Original Platform
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README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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A fine-tuned version of Mistral-7B-Instruct-v0.3, trained to convert complex medical prescriptions into simple,
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patient-friendly explanations. Fine-tuned using QLoRA on Kaggle T4×2 GPUs with 8 hand-crafted prescription→explanation training
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examples covering Amoxicillin, Metformin, Lisinopril, Atorvastatin, Salbutamol, Sertraline, Warfarin, and Pantoprazole.
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- **Developed by:** Madhukar Kumar
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- **Funded by [optional]:** Self Funded
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- **Shared by [optional]:** Madhukar Kumar
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- **Model type:** Causal Language Model (LLM) — fine-tuned using QLoRA (4-bit NF4 quantization + LoRA adapters) on mistralai/Mistral-7B-Instruct-v0.3
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0 (inherited from Mistral-7B-Instruct-v0.3)
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- **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.3
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### Model Sources [optional]
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- **Repository:** (https://huggingface.co/integration1857/prescription-simplifier-mistral7b)
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- **Paper [optional]:** https://medium.com/p/49e94536f72b
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- **Demo [optional]:** (https://d6zhmy6z4ifp0.cloudfront.net)
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## Uses
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### Direct Use
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This model is designed to convert complex medical prescription text into simple, plain-language explanations for patients.
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It can be used directly via the HuggingFace Inference API or integrated into healthcare applications, pharmacy portals,
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or patient-facing tools.
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### Downstream Use [optional]
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Can be fine-tuned further on larger prescription datasets for improved accuracy.
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Can be integrated into hospital information systems, pharmacy apps, or telemedicine platforms to improve patient health literacy.
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### Out-of-Scope Use
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This model is NOT intended for clinical decision-making, medical diagnosis, or treatment recommendations.
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It should not replace professional medical advice. Not suitable for prescriptions in languages other than English.
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## Bias, Risks, and Limitations
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Trained on only 8 examples — limited coverage of drug types and medical conditions
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May produce inaccurate explanations for uncommon medications
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Does not account for patient-specific factors such as allergies or drug interactions
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English only — not suitable for multilingual use cases
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Should always be used alongside professional medical guidance
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### Recommendations
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Always display a medical disclaimer alongside model outputs.
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Never use this model as a substitute for pharmacist or physician consultation.
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Outputs should be reviewed by a healthcare professional before deployment in clinical settings.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
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More information needed for further recommendations.
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## How to Get Started with the Model
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "integration1857/prescription-simplifier-mistral7b"
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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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prompt = """[INST] Convert this prescription into patient-friendly language:
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Amoxicillin 500mg TID x 7 days [/INST]"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.3)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## Training Details
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### Training Data
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8 hand-crafted prescription→explanation pairs covering: Amoxicillin, Metformin, Lisinopril, Atorvastatin, Salbutamol, Sertraline, Warfarin, and Pantoprazole.
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Each example follows the Mistral [INST]...[/INST] instruction format.
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### Training Procedure
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#### Preprocessing [optional]
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Prescriptions formatted using Mistral instruct template.
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Tokenized using the Mistral-7B-v0.3 tokenizer with a maximum sequence length of 512 tokens.
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#### Training Hyperparameters
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- **Training regime:**
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bf16 mixed precision
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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, v_proj, k_proj, o_proj
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Batch size: 2
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Gradient accumulation steps: 4
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Learning rate: 2e-4
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Epochs: 3
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Optimizer: paged_adamw_32bit
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#### Speeds, Sizes, Times [optional]
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Training time: 4.6 minutes
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Hardware: Kaggle T4×2 GPUs (16GB VRAM each)
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Trainable parameters: 41.94M (1.11% of 3.78B total)
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Base model size: ~14GB (fp16)
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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Held-out prescription examples not seen during training, covering similar drug categories.
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#### Factors
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Evaluated on clarity of explanation, accuracy of dosage instructions, and completeness of warnings.
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#### Metrics
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ROUGE-1 score used to measure overlap between generated explanations and reference explanations.
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### Results
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ROUGE-1: 0.51
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Training loss: 1.78
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#### Summary
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The model achieves reasonable performance on the prescription simplification task given the very small training set.
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A larger, more diverse dataset would significantly improve generalisation.
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## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [NVIDIA Tesla T4 ×2]
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- **Hours used:** [0.08 hours (4.6 minutes)]
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- **Cloud Provider:** [Kaggle (Google Cloud backend)]
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- **Compute Region:** [US]
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- **Carbon Emitted:** [(< 0.01 kg CO₂ estimated)]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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Based on Mistral-7B-Instruct-v0.3 (decoder-only transformer).
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Fine-tuned with QLoRA — 4-bit NF4 quantization via bitsandbytes with LoRA adapter layers injected into attention modules.
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Objective: causal language modelling on prescription→explanation pairs.
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### Compute Infrastructure
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Kaggle Notebooks — free tier T4×2 GPU environment.
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#### Hardware
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2× NVIDIA Tesla T4 (16GB VRAM each)
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#### Software
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transformers, peft, trl, bitsandbytes, accelerate, torch 2.0
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## Citation [optional]
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**BibTeX:**
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@misc{prescription-simplifier-2025,
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author = {integration1857},
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title = {Prescription Simplifier — Mistral-7B Fine-tuned for Patient-Friendly Medical Explanations},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/integration1857/prescription-simplifier-mistral7b}
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}
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**APA:**
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integration1857. (2025).Prescription Simplifier — Mistral-7B Fine-tuned for Patient-Friendly Medical Explanations.
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HuggingFace. https://huggingface.co/integration1857/prescription-simplifier-mistral7b
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## Glossary [optional]
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https://medium.com/p/49e94536f72b
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## More Information [optional]
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||||
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(https://www.linkedin.com/pulse/doctors-write-code-i-built-ai-translate-madhukar-kumar-3f9cc/?trackingId=v%2BIJoWuLQPuJPt0yNtxe6Q%3D%3D)
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## Model Card Authors [optional]
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Madhukar Kumar
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## Model Card Contact
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Via HuggingFace profile: https://huggingface.co/integration1857
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chat_template.jinja
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{%- if messages[0]["role"] == "system" %}
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{%- set system_message = messages[0]["content"] %}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set loop_messages = messages %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}
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{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}
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{%- set ns = namespace() %}
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{%- set ns.index = 0 %}
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{%- for message in loop_messages %}
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{%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
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{%- if (message["role"] == "user") != (ns.index % 2 == 0) %}
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{{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif %}
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{%- set ns.index = ns.index + 1 %}
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{%- endif %}
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{%- endfor %}
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{{- bos_token }}
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{%- for message in loop_messages %}
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{%- if message["role"] == "user" %}
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{%- if tools is not none and (message == user_messages[-1]) %}
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{{- "[AVAILABLE_TOOLS] [" }}
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{%- for tool in tools %}
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{%- set tool = tool.function %}
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{{- '{"type": "function", "function": {' }}
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{%- for key, val in tool.items() if key != "return" %}
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{%- if val is string %}
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{{- '"' + key + '": "' + val + '"' }}
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{%- else %}
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{{- '"' + key + '": ' + val|tojson }}
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{%- endif %}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- "}}" }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" }}
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{%- endif %}
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{%- endfor %}
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{{- "[/AVAILABLE_TOOLS]" }}
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{%- endif %}
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{%- if loop.last and system_message is defined %}
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{{- "[INST] " + system_message + "\n\n" + message["content"] + "[/INST]" }}
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{%- else %}
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{{- "[INST] " + message["content"] + "[/INST]" }}
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{%- endif %}
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{%- elif message.tool_calls is defined and message.tool_calls is not none %}
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{{- "[TOOL_CALLS] [" }}
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{%- for tool_call in message.tool_calls %}
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{%- set out = tool_call.function|tojson %}
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{{- out[:-1] }}
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{%- if not tool_call.id is defined or tool_call.id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- ', "id": "' + tool_call.id + '"}' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" + eos_token }}
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{%- endif %}
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{%- endfor %}
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{%- elif message["role"] == "assistant" %}
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{{- " " + message["content"]|trim + eos_token}}
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{%- elif message["role"] == "tool_results" or message["role"] == "tool" %}
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{%- if message.content is defined and message.content.content is defined %}
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{%- set content = message.content.content %}
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{%- else %}
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{%- set content = message.content %}
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{%- endif %}
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{{- '[TOOL_RESULTS] {"content": ' + content|string + ", " }}
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{%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }}
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{%- else %}
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{{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}
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{%- endif %}
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{%- endfor %}
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config.json
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{
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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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"dtype": "float16",
|
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 32768,
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"model_type": "mistral",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
|
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"pad_token_id": null,
|
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"rms_norm_eps": 1e-05,
|
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"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
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"rope_type": "default"
|
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},
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "5.0.0",
|
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"use_cache": true,
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"vocab_size": 32768
|
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "5.0.0"
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}
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}
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||||
}
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||||
275733
tokenizer.json
Normal file
275733
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
17
tokenizer_config.json
Normal file
17
tokenizer_config.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"add_prefix_space": true,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<s>",
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||||
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||||
"eos_token": "</s>",
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||||
"is_local": true,
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||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
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||||
"pad_token": "</s>",
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||||
"padding_side": "right",
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||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user