What are the hardware requirements for Doctor Llama 160M?

Doctor Llama 160M requires a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Doctor Llama 160M and how large is it?

Doctor Llama 160M is developed by mmoreirast, a model with 160m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Doctor Llama 160M?

Doctor Llama 160M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Doctor Llama 160M by mmoreirast — 2K context

Name: Doctor Llama 160M
Author: mmoreirast

Doctor Llama 160M is an open-source language model by mmoreirast. Features: 160m LLM, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.13.

Conversational Dataset:mmoreirast/medicine-ev... Dataset:mmoreirast/medicine-tr... Endpoints compatible Llama Llama2 Medicine Pt Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mmoreirast/Doctor-Llama-160m

Doctor Llama 160M Parameters and Internals

Model Type

fine-tuned, text generation

Use Cases

Areas:

research

Applications:

medical fine-tuning in Portuguese

Primary Use Cases:

study step-by-step process in fine-tuning models

Limitations:

hallucinations, biases and toxicity, unreliable code, language limitations, repetition and verbosity

Considerations:

Models should not be used in human-facing interactions without oversight.

Additional Notes

The Doctor Llama model continues the research in fine-tuning language models specifically in the medical domain in Portuguese.

Supported Languages

Portuguese (High proficiency)

Training Details

Data Sources:

medicine-training-pt, medicine-evaluation-pt

Methodology:

Fine-tuning

Context Length:

2048

Training Time:

~9 hours

Hardware Used:

NVIDIA L4 GPU

Safety Evaluation

Risk Categories:

hallucination, bias, toxicity

Ethical Considerations:

The model can produce biased or toxic content inherited from the training data.

Responsible Ai Considerations

Accountability:

Users are responsible for conducting risk and bias assessments.

Mitigation Strategies:

Users should perform risk analysis and have human oversight in applications.

Input Output

Input Format:

text

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Ensure proper prompt structure and moderation of outputs.

LLM Name	Doctor Llama 160M
Repository 🤗	https://huggingface.co/mmoreirast/Doctor-Llama-160m
Model Size	160m
Required VRAM	0 GB
Updated	2026-07-29
Maintainer	mmoreirast
Model Type	llama
Model Files	0.7 GB 1.3 GB 0.0 GB 0.0 GB
Supported Languages	pt
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.38.0
Tokenizer Class	LlamaTokenizer
Padding Token	<pad>
Vocabulary Size	32002
Torch Data Type	float32

Rank the Doctor Llama 160M capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Doctor Llama 160M

Best Alternatives	Context / RAM	Downloads	Likes
Llama 160M	2K / 0 GB	265161	37
Llama 160M Chat V1	2K / 0.7 GB	954	20
Mergekit Passthrough Jvjpjlj	2K / 0.4 GB	6	0
Tucano 160M	2K / 0.3 GB	351	4
TeenyTinyLlama Doc 160M	2K / 0 GB	10	0
Vicuna 160M	2K / 0.7 GB	28	2
TeenyTinyLlama 160M	2K / 0.7 GB	77	8
Onnx Llama 160M Chat V1	2K / GB	4	1
Spider Llama 160M	2K / 0.7 GB	6	0
Llama 160M Base	2K / 0 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than mmoreirast/Doctor-Llama-160m.