Instructions to use yixuantt/InvestLM-mistral-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yixuantt/InvestLM-mistral-AWQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yixuantt/InvestLM-mistral-AWQ")
model = AutoModelForCausalLM.from_pretrained("yixuantt/InvestLM-mistral-AWQ")
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 yixuantt/InvestLM-mistral-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yixuantt/InvestLM-mistral-AWQ

SGLang

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

Docker Model Runner
How to use yixuantt/InvestLM-mistral-AWQ with Docker Model Runner:
```
docker model run hf.co/yixuantt/InvestLM-mistral-AWQ
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

InvestLM

This is the repo for a new financial domain large language model, InvestLM, tuned on Mixtral-8x7B-v0.1, using a carefully curated instruction dataset related to financial investment. We provide guidance on how to use InvestLM for inference.

Github Link: InvestLM

Test only, not for sharing.

About AWQ

AWQ is an efficient, accurate, and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.

Inference

Please use the following command to log in hugging face first.

huggingface-cli login

Prompt template

[INST] {prompt} [/INST]

How to use this AWQ model from Python code

pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"

from transformers import AutoTokenizer, TextStreamer

quant_path = "yixuantt/InvestLM-Mistral-AWQ"

# Load model
model = AutoModelForCausalLM.from_pretrained(
    quant_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)
tokenizer = AutoTokenizer.from_pretrained(quant_path)

# Convert prompt to tokens
prompt_template = "[INST] {prompt} [/INST]"
prompt = "What is finance?"

tokens = tokenizer(
    prompt_template.format(prompt=prompt), 
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    max_new_tokens = 512
)

print("Output: ", tokenizer.decode(generation_output[0]))

Downloads last month: -

Safetensors

Model size

47B params

Tensor type

I32

F16