RichardErkhov/johnsnowlabs_-_JSL-MedLlama-3-8B-v1.0-gguf

Quantization made by Richard Erkhov.

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JSL-MedLlama-3-8B-v1.0 - GGUF

• Model creator: https://huggingface.co/johnsnowlabs/
• Original model: https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0/

Name	Quant method	Size
JSL-MedLlama-3-8B-v1.0.Q2_K.gguf	Q2_K	2.96GB
JSL-MedLlama-3-8B-v1.0.IQ3_XS.gguf	IQ3_XS	3.28GB
JSL-MedLlama-3-8B-v1.0.IQ3_S.gguf	IQ3_S	3.43GB
JSL-MedLlama-3-8B-v1.0.Q3_K_S.gguf	Q3_K_S	3.41GB
JSL-MedLlama-3-8B-v1.0.IQ3_M.gguf	IQ3_M	3.52GB
JSL-MedLlama-3-8B-v1.0.Q3_K.gguf	Q3_K	3.74GB
JSL-MedLlama-3-8B-v1.0.Q3_K_M.gguf	Q3_K_M	3.74GB
JSL-MedLlama-3-8B-v1.0.Q3_K_L.gguf	Q3_K_L	4.03GB
JSL-MedLlama-3-8B-v1.0.IQ4_XS.gguf	IQ4_XS	4.18GB
JSL-MedLlama-3-8B-v1.0.Q4_0.gguf	Q4_0	4.34GB
JSL-MedLlama-3-8B-v1.0.IQ4_NL.gguf	IQ4_NL	4.38GB
JSL-MedLlama-3-8B-v1.0.Q4_K_S.gguf	Q4_K_S	4.37GB
JSL-MedLlama-3-8B-v1.0.Q4_K.gguf	Q4_K	4.58GB
JSL-MedLlama-3-8B-v1.0.Q4_K_M.gguf	Q4_K_M	4.58GB
JSL-MedLlama-3-8B-v1.0.Q4_1.gguf	Q4_1	4.78GB
JSL-MedLlama-3-8B-v1.0.Q5_0.gguf	Q5_0	5.21GB
JSL-MedLlama-3-8B-v1.0.Q5_K_S.gguf	Q5_K_S	5.21GB
JSL-MedLlama-3-8B-v1.0.Q5_K.gguf	Q5_K	5.34GB
JSL-MedLlama-3-8B-v1.0.Q5_K_M.gguf	Q5_K_M	5.34GB
JSL-MedLlama-3-8B-v1.0.Q5_1.gguf	Q5_1	5.65GB
JSL-MedLlama-3-8B-v1.0.Q6_K.gguf	Q6_K	6.14GB
JSL-MedLlama-3-8B-v1.0.Q8_0.gguf	Q8_0	7.95GB

Original model description:

tags: - llama-3-8b - sft - medical base_model: - meta-llama/Meta-Llama-3-8B license: cc-by-nc-nd-4.0

JSL-MedLlama-3-8B-v1.0

This model is developed by John Snow Labs.

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

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johnsnowlabs/JSL-MedLlama-3-8B-v1.0"
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	n-shot	Metric	Value		Stderr
stem	N/A	none	0	acc	0.6217	±	0.0057
		none	0	acc_norm	0.5847	±	0.0066
- medmcqa	Yaml	none	0	acc	0.5563	±	0.0077
		none	0	acc_norm	0.5563	±	0.0077
- medqa_4options	Yaml	none	0	acc	0.6779	±	0.0131
		none	0	acc_norm	0.6779	±	0.0131
- anatomy (mmlu)	0	none	0	acc	0.6963	±	0.0397
- clinical_knowledge (mmlu)	0	none	0	acc	0.7509	±	0.0266
- college_biology (mmlu)	0	none	0	acc	0.7986	±	0.0335
- college_medicine (mmlu)	0	none	0	acc	0.6590	±	0.0361
- medical_genetics (mmlu)	0	none	0	acc	0.8500	±	0.0359
- professional_medicine (mmlu)	0	none	0	acc	0.7868	±	0.0249
- pubmedqa	1	none	0	acc	0.7380	±	0.0197

Groups	Version	Filter	n-shot	Metric	Value		Stderr
stem	N/A	none	0	acc	0.6217	±	0.0057
		none	0	acc_norm	0.5847	±	0.0066