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1!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "johnsnowlabs/JSL-MedMNX-7B"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(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 |