Instructions to use Writer/Palmyra-Med-70B-32K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Writer/Palmyra-Med-70B-32K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Writer/Palmyra-Med-70B-32K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Writer/Palmyra-Med-70B-32K")
model = AutoModelForCausalLM.from_pretrained("Writer/Palmyra-Med-70B-32K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Writer/Palmyra-Med-70B-32K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Writer/Palmyra-Med-70B-32K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/Palmyra-Med-70B-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Writer/Palmyra-Med-70B-32K

SGLang

How to use Writer/Palmyra-Med-70B-32K with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Writer/Palmyra-Med-70B-32K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/Palmyra-Med-70B-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Writer/Palmyra-Med-70B-32K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/Palmyra-Med-70B-32K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Writer/Palmyra-Med-70B-32K with Docker Model Runner:
```
docker model run hf.co/Writer/Palmyra-Med-70B-32K
```

How about a scaled down version like 7B/8B?

by ZeroWw - opened Aug 2, 2024

Discussion

ZeroWw

Aug 2, 2024

How about a scaled down version like 7B/8B?

gileneo

Aug 7, 2024

7/8B versions are not good for complex medical reasoning, that's my experience with using Llama 3.1 8B. So here might be similar....

ZeroWw

Aug 7, 2024

7/8B versions are not good for complex medical reasoning, that's my experience with using Llama 3.1 8B. So here might be similar....

Mistral 7B v03 was not bad in medical reasoning (as it was) with finetuning it could improve a lot.

AdityaKothari

Sep 5, 2024

7/8B versions are not good for complex medical reasoning, that's my experience with using Llama 3.1 8B. So here might be similar....

Mistral 7B v03 was not bad in medical reasoning (as it was) with finetuning it could improve a lot.
I'm planning to finetune Mistral 7B v0.3 4bit quantized for nutrition, diet and exercise recommendation. Can you recommend any datasets I can use for this task?

qizhang2004

Feb 27, 2025

Looking forward to the distilled version. If the version can be distilled and deployed locally in medical colleges, it should benefit many people.

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