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RichardErkhov/johnsnowlabs_-_JSL-MedPhi2-2.7B-gguf
johnsnowlabs_-_JSL-MedPhi2-2.7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf37.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
JSL-MedPhi2-2.7B - GGUF
| Name | Quant method | Size |
|---|---|---|
| JSL-MedPhi2-2.7B.Q2_K.gguf | Q2_K | 1.03GB |
| JSL-MedPhi2-2.7B.IQ3_XS.gguf | IQ3_XS | 1.12GB |
| JSL-MedPhi2-2.7B.IQ3_S.gguf | IQ3_S | 1.16GB |
| JSL-MedPhi2-2.7B.Q3_K_S.gguf | Q3_K_S | 1.16GB |
| JSL-MedPhi2-2.7B.IQ3_M.gguf | IQ3_M | 1.23GB |
| JSL-MedPhi2-2.7B.Q3_K.gguf | Q3_K | 1.33GB |
| JSL-MedPhi2-2.7B.Q3_K_M.gguf | Q3_K_M | 1.33GB |
| JSL-MedPhi2-2.7B.Q3_K_L.gguf | Q3_K_L | 1.47GB |
| JSL-MedPhi2-2.7B.IQ4_XS.gguf | IQ4_XS | 1.43GB |
| JSL-MedPhi2-2.7B.Q4_0.gguf | Q4_0 | 1.49GB |
| JSL-MedPhi2-2.7B.IQ4_NL.gguf | IQ4_NL | 1.5GB |
| JSL-MedPhi2-2.7B.Q4_K_S.gguf | Q4_K_S | 1.51GB |
| JSL-MedPhi2-2.7B.Q4_K.gguf | Q4_K | 1.62GB |
| JSL-MedPhi2-2.7B.Q4_K_M.gguf | Q4_K_M | 1.62GB |
| JSL-MedPhi2-2.7B.Q4_1.gguf | Q4_1 | 1.65GB |
| JSL-MedPhi2-2.7B.Q5_0.gguf | Q5_0 | 1.8GB |
| JSL-MedPhi2-2.7B.Q5_K_S.gguf | Q5_K_S | 1.8GB |
| JSL-MedPhi2-2.7B.Q5_K.gguf | Q5_K | 1.87GB |
| JSL-MedPhi2-2.7B.Q5_K_M.gguf | Q5_K_M | 1.87GB |
| JSL-MedPhi2-2.7B.Q5_1.gguf | Q5_1 | 1.95GB |
| JSL-MedPhi2-2.7B.Q6_K.gguf | Q6_K | 2.13GB |
| JSL-MedPhi2-2.7B.Q8_0.gguf | Q8_0 | 2.75GB |
tags:
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 [email protected].
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedPhi2-2.7B"
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"])
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| stem | N/A | none | 0 | acc_norm | 0.3904 | ± | 0.0066 |
| none | 0 | acc | 0.4402 | ± | 0.0058 | ||
| - medmcqa | Yaml | none | 0 | acc | 0.3899 | ± | 0.0075 |
| none | 0 | acc_norm | 0.3899 | ± | 0.0075 | ||
| - medqa_4options | Yaml | none | 0 | acc | 0.3920 | ± | 0.0137 |
| none | 0 | acc_norm | 0.3920 | ± | 0.0137 | ||
| - anatomy (mmlu) | 0 | none | 0 | acc | 0.4815 | ± | 0.0432 |
| - clinical_knowledge (mmlu) | 0 | none | 0 | acc | 0.6340 | ± | 0.0296 |
| - college_biology (mmlu) | 0 | none | 0 | acc | 0.6181 | ± | 0.0406 |
| - college_medicine (mmlu) | 0 | none | 0 | acc | 0.5665 | ± | 0.0378 |
| - medical_genetics (mmlu) | 0 | none | 0 | acc | 0.6300 | ± | 0.0485 |
| - professional_medicine (mmlu) | 0 | none | 0 | acc | 0.4522 | ± | 0.0302 |
| - pubmedqa | 1 | none | 0 | acc | 0.7300 | ± | 0.0199 |
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| stem | N/A | none | 0 | acc_norm | 0.3904 | ± | 0.0066 |
| none | 0 | acc | 0.4402 | ± | 0.0058 |