Instructions to use FPHam/Writing_Partner_Mistral_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FPHam/Writing_Partner_Mistral_7B with Transformers:

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

pipe = pipeline("text-generation", model="FPHam/Writing_Partner_Mistral_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FPHam/Writing_Partner_Mistral_7B")
model = AutoModelForCausalLM.from_pretrained("FPHam/Writing_Partner_Mistral_7B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FPHam/Writing_Partner_Mistral_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FPHam/Writing_Partner_Mistral_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FPHam/Writing_Partner_Mistral_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FPHam/Writing_Partner_Mistral_7B

SGLang

How to use FPHam/Writing_Partner_Mistral_7B 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 "FPHam/Writing_Partner_Mistral_7B" \
    --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": "FPHam/Writing_Partner_Mistral_7B",
		"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 "FPHam/Writing_Partner_Mistral_7B" \
        --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": "FPHam/Writing_Partner_Mistral_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FPHam/Writing_Partner_Mistral_7B with Docker Model Runner:
```
docker model run hf.co/FPHam/Writing_Partner_Mistral_7B
```

Quantized version?

by drakosfire - opened Nov 19, 2023

Discussion

drakosfire

Nov 19, 2023

Hello! I had downloaded your model and was playing with it, I quite like it and wanted to share it with someone to run locally on a 3080 (10 Gb of VRAM). I was thinking about quantizing it, and learned that to quantize you need the original dataset it was trained on. Would you be open to quantizing and sharing the model or else sharing what data set you had fine tuned it on?

dzaut

Dec 30, 2023

TheBloke has quantized this model in:

GGUF , AWQ , and GPTQ formats.

I've also quantized this model in EXL2 if you prefer that format at 3.0bpw , and 4.0bpw . I've been using the 4.0bpw EXL2 quant on my 3080 12gb GPU via oobabooga and the ExLlamav2_HF loader.

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