Who developed Llama 2 Medical Consultation and how large is it?

Llama 2 Medical Consultation is developed by Ashishkr, a model with 7b 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 Llama 2 Medical Consultation?

Llama 2 Medical Consultation is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 2 Medical Consultation by Ashishkr

Name: Llama 2 Medical Consultation
Author: Ashishkr

Llama 2 Medical Consultation is an open-source language model by Ashishkr. Features: 7b LLM, Quantized, LLM Explorer Score: 0.08.

Adapter Autotrain Base model:adapter:meta-llama/... Base model:meta-llama/llama-2-... Finetuned Ggml Lora Meta-llama Meta-llama/llama-2-7b-hf Peft Quantized Region:us Tensorboard

Model Card on HF 🤗: https://huggingface.co/Ashishkr/llama-2-medical-consultation

Llama 2 Medical Consultation Parameters and Internals

Model Type

text generation

Additional Notes

Model is fine-tuned for medical consultation

Input Output

Input Format:

Instruction and input text for medical consultation

Accepted Modalities:

text

Output Format:

Generated response text

Performance Tips:

Use recommended hardware for optimal performance.

LLM Name	Llama 2 Medical Consultation
Repository 🤗	https://huggingface.co/Ashishkr/llama-2-medical-consultation
Base Model(s)	Llama 2 7B Hf meta-llama/Llama-2-7b-hf
Model Size	7b
Required VRAM	0 GB
Updated	2026-09-18
Maintainer	Ashishkr
Model Files	0.0 GB 0.0 GB 0.0 GB
GGML Quantization	Yes
Quantization Type	ggml
Model Architecture	Adapter
Model Max Length	1024
Is Biased	none
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|v_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Llama 2 Medical Consultation 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

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Note: green Score (e.g. "73.2") means that the model is better than Ashishkr/llama-2-medical-consultation.