Instructions to use Moses25/Mistral-7B-Instruct-32K-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Moses25/Mistral-7B-Instruct-32K-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="Moses25/Mistral-7B-Instruct-32K-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Moses25/Mistral-7B-Instruct-32K-AWQ")
model = AutoModelForCausalLM.from_pretrained("Moses25/Mistral-7B-Instruct-32K-AWQ", device_map="auto")
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 Settings

vLLM

How to use Moses25/Mistral-7B-Instruct-32K-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Moses25/Mistral-7B-Instruct-32K-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": "Moses25/Mistral-7B-Instruct-32K-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Moses25/Mistral-7B-Instruct-32K-AWQ

SGLang

How to use Moses25/Mistral-7B-Instruct-32K-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 "Moses25/Mistral-7B-Instruct-32K-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": "Moses25/Mistral-7B-Instruct-32K-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 "Moses25/Mistral-7B-Instruct-32K-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": "Moses25/Mistral-7B-Instruct-32K-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Moses25/Mistral-7B-Instruct-32K-AWQ with Docker Model Runner:
```
docker model run hf.co/Moses25/Mistral-7B-Instruct-32K-AWQ
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer


quant_path = "Moses25/Mistral-7B-Instruct-32K-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

chat = [
    {"role": "system", "content": "You are a concise assistant that helps answer questions."},
    {"role": "user", "content": prompt},
]

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("</s>")
]

tokens = tokenizer.apply_chat_template(
    chat,
    return_tensors="pt"
).cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=2048,
    eos_token_id=terminators
)

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Safetensors

Model size

7B params

Tensor type

I32

F16

Space using Moses25/Mistral-7B-Instruct-32K-AWQ 1

Collection including Moses25/Mistral-7B-Instruct-32K-AWQ

Mistral-7B-V0.2

Collection

4 items • Updated Jun 5, 2024