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RichardErkhov/
johnsnowlabs_-_JSL-MedMNX-7B-v2.0-gguf

Name: johnsnowlabs_-_JSL-MedMNX-7B-v2.0-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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JSL-MedMNX-7B-v2.0 - GGUF

Model creator: https://huggingface.co/johnsnowlabs/
Original model: https://huggingface.co/johnsnowlabs/JSL-MedMNX-7B-v2.0/

Name	Quant method	Size
JSL-MedMNX-7B-v2.0.Q2_K.gguf	Q2_K	2.53GB
JSL-MedMNX-7B-v2.0.IQ3_XS.gguf	IQ3_XS	2.81GB
JSL-MedMNX-7B-v2.0.IQ3_S.gguf	IQ3_S	2.96GB
JSL-MedMNX-7B-v2.0.Q3_K_S.gguf	Q3_K_S	2.95GB
JSL-MedMNX-7B-v2.0.IQ3_M.gguf	IQ3_M	3.06GB
JSL-MedMNX-7B-v2.0.Q3_K.gguf	Q3_K	3.28GB
JSL-MedMNX-7B-v2.0.Q3_K_M.gguf	Q3_K_M	3.28GB
JSL-MedMNX-7B-v2.0.Q3_K_L.gguf	Q3_K_L	3.56GB
JSL-MedMNX-7B-v2.0.IQ4_XS.gguf	IQ4_XS	3.67GB
JSL-MedMNX-7B-v2.0.Q4_0.gguf	Q4_0	3.83GB
JSL-MedMNX-7B-v2.0.IQ4_NL.gguf	IQ4_NL	3.87GB
JSL-MedMNX-7B-v2.0.Q4_K_S.gguf	Q4_K_S	3.86GB
JSL-MedMNX-7B-v2.0.Q4_K.gguf	Q4_K	4.07GB
JSL-MedMNX-7B-v2.0.Q4_K_M.gguf	Q4_K_M	4.07GB
JSL-MedMNX-7B-v2.0.Q4_1.gguf	Q4_1	4.24GB
JSL-MedMNX-7B-v2.0.Q5_0.gguf	Q5_0	4.65GB
JSL-MedMNX-7B-v2.0.Q5_K_S.gguf	Q5_K_S	4.65GB
JSL-MedMNX-7B-v2.0.Q5_K.gguf	Q5_K	4.78GB
JSL-MedMNX-7B-v2.0.Q5_K_M.gguf	Q5_K_M	4.78GB
JSL-MedMNX-7B-v2.0.Q5_1.gguf	Q5_1	5.07GB
JSL-MedMNX-7B-v2.0.Q6_K.gguf	Q6_K	5.53GB
JSL-MedMNX-7B-v2.0.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: cc-by-nc-nd-4.0 language:

en library_name: transformers tags:
reward model
RLHF
medical

JSL-MedMNX-7B-v2.0

This model is developed by John Snow Labs. Performance on biomedical benchmarks: Open Medical LLM Leaderboard.

This model is available under a CC-BY-NC-ND license and must also conform to this Acceptable Use Policy. If you need to license this model for commercial use, please contact us at info@johnsnowlabs.com.

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johnsnowlabs/JSL-MedMNX-7B-v2.0"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

🏆 Evaluation

Tasks	Version	Filter	Metric	Value		Stderr
stem	N/A	none	acc	0.6085	±	0.0057
		none	acc_norm	0.5700	±	0.0067
- medmcqa	Yaml	none	acc	0.5625	±	0.0077
		none	acc_norm	0.5625	±	0.0077
- medqa_4options	Yaml	none	acc	0.5947	±	0.0138
		none	acc_norm	0.5947	±	0.0138
- anatomy (mmlu)	0	none	acc	0.6444	±	0.0414
- clinical_knowledge (mmlu)	0	none	acc	0.7509	±	0.0266
- college_biology (mmlu)	0	none	acc	0.7639	±	0.0355
- college_medicine (mmlu)	0	none	acc	0.6532	±	0.0363
- medical_genetics (mmlu)	0	none	acc	0.7500	±	0.0435
- professional_medicine (mmlu)	0	none	acc	0.7537	±	0.0262
- pubmedqa	1	none	acc	0.7760	±	0.0187

Groups	Version	Filter	n-shot	Metric	Value		Stderr
stem	N/A	none	0	acc	0.6085	±	0.0057
		none	0	acc_norm	0.5700	±	0.0067

RichardErkhov/johnsnowlabs_-_JSL-MedMNX-7B-v2.0-gguf

Original model description:

JSL-MedMNX-7B-v2.0

💻 Usage

🏆 Evaluation

RichardErkhov/
johnsnowlabs_-_JSL-MedMNX-7B-v2.0-gguf