Instructions to use Writer/InstructPalmyra-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Writer/InstructPalmyra-20b with Transformers:

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

pipe = pipeline("text-generation", model="Writer/InstructPalmyra-20b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Writer/InstructPalmyra-20b")
model = AutoModelForCausalLM.from_pretrained("Writer/InstructPalmyra-20b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Writer/InstructPalmyra-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Writer/InstructPalmyra-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/InstructPalmyra-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Writer/InstructPalmyra-20b

SGLang

How to use Writer/InstructPalmyra-20b 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/InstructPalmyra-20b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/InstructPalmyra-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/InstructPalmyra-20b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/InstructPalmyra-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Writer/InstructPalmyra-20b with Docker Model Runner:
```
docker model run hf.co/Writer/InstructPalmyra-20b
```

wassemgtk commited on Aug 28, 2023

Commit

f91439a

1 Parent(s): af3875b

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -1

README.md CHANGED Viewed

@@ -5,12 +5,21 @@ language:
 tags:
 - InstructGPT
 - hf
 ---
 # InstructPalmyra-20b
 <style>
 img {
  display: inline;
@@ -82,6 +91,22 @@ InstructPalmyra's core functionality is to take a string of text and predict the
 InstructPalmyra was trained on Writer’s custom data. As with all language models, it is difficult to predict how InstructPalmyra will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
 ## Citation and Related Information
@@ -93,7 +118,7 @@ To cite this model:
   title = {{InstructPalmyra-20b : Instruct tuned Palmyra-Large model}},
   howpublished = {\url{https://dev.writer.com}},
   year = 2023,
-  month = July
 }
 ```
 [![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)|[![Model size](https://img.shields.io/badge/Params-20B-green)](#model-architecture)|[![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)|![AUR license](https://img.shields.io/badge/license-Apache%202-blue)

 tags:
 - InstructGPT
 - hf
+datasets:
+- Writer/palmyra-data-index
 ---
 # InstructPalmyra-20b
+- **Developed by:** [https://writer.com/](https://writer.com/);
+- **Model type:** Causal decoder-only;
+- **Language(s) (NLP):** English;
+- **License:** Apache 2.0;
+- **Finetuned from model:** [Palmyra-20B](https://huggingface.co/Writer/palmyra-large).
 <style>
 img {
  display: inline;
 InstructPalmyra was trained on Writer’s custom data. As with all language models, it is difficult to predict how InstructPalmyra will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
+## Uses
+### Out-of-Scope Use
+Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
+## Bias, Risks, and Limitations
+Palmyra-Med-20B is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
+### Recommendations
+We recommend users of Palmyra-Med-20B to develop guardrails and to take appropriate precautions for any production use.
 ## Citation and Related Information
   title = {{InstructPalmyra-20b : Instruct tuned Palmyra-Large model}},
   howpublished = {\url{https://dev.writer.com}},
   year = 2023,
+  month = Augest
 }
 ```
 [![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)|[![Model size](https://img.shields.io/badge/Params-20B-green)](#model-architecture)|[![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)|![AUR license](https://img.shields.io/badge/license-Apache%202-blue)