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RichardErkhov/johnsnowlabs_-_JSL-MedMNX-7B-gguf
johnsnowlabs_-_JSL-MedMNX-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.
Downloads · 30 days
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From the Hugging Face model README
Quantization made by Richard Erkhov.
JSL-MedMNX-7B - GGUF
| Name | Quant method | Size |
|---|---|---|
| JSL-MedMNX-7B.Q2_K.gguf | Q2_K | 2.53GB |
| JSL-MedMNX-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| JSL-MedMNX-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| JSL-MedMNX-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| JSL-MedMNX-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| JSL-MedMNX-7B.Q3_K.gguf | Q3_K | 3.28GB |
| JSL-MedMNX-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| JSL-MedMNX-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| JSL-MedMNX-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| JSL-MedMNX-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| JSL-MedMNX-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| JSL-MedMNX-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| JSL-MedMNX-7B.Q4_K.gguf | Q4_K | 4.07GB |
| JSL-MedMNX-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| JSL-MedMNX-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| JSL-MedMNX-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| JSL-MedMNX-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| JSL-MedMNX-7B.Q5_K.gguf | Q5_K | 4.78GB |
| JSL-MedMNX-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| JSL-MedMNX-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| JSL-MedMNX-7B.Q6_K.gguf | Q6_K | 5.53GB |
| JSL-MedMNX-7B.Q8_0.gguf | Q8_0 | 7.17GB |
license: cc-by-nc-nd-4.0 language:
JSL-MedMNX-7B is a 7 Billion parameter model developed by John Snow Labs. This model is trained on medical datasets to provide state-of-the-art 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 [email protected].
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedMNX-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 | 0.5191 | ± | 0.0068 |
| none | 0 | acc | 0.5658 | ± | 0.0058 | ||
| - medmcqa | Yaml | none | 0 | acc | 0.5135 | ± | 0.0077 |
| none | 0 | acc_norm | 0.5135 | ± | 0.0077 | ||
| - medqa_4options | Yaml | none | 0 | acc | 0.5373 | ± | 0.0140 |
| none | 0 | acc_norm | 0.5373 | ± | 0.0140 | ||
| - anatomy (mmlu) | 0 | none | 0 | acc | 0.6370 | ± | 0.0415 |
| - clinical_knowledge (mmlu) | 0 | none | 0 | acc | 0.7245 | ± | 0.0275 |
| - college_biology (mmlu) | 0 | none | 0 | acc | 0.7500 | ± | 0.0362 |
| - college_medicine (mmlu) | 0 | none | 0 | acc | 0.6590 | ± | 0.0361 |
| - medical_genetics (mmlu) | 0 | none | 0 | acc | 0.7200 | ± | 0.0451 |
| - professional_medicine (mmlu) | 0 | none | 0 | acc | 0.7206 | ± | 0.0273 |
| - pubmedqa | 1 | none | 0 | acc | 0.7720 | ± | 0.0188 |
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| stem | N/A | none | 0 | acc_norm | 0.5191 | ± | 0.0068 |
| none | 0 | acc | 0.5658 | ± | 0.0058 |