Instructions to use TRAC-MTRY/traclm-v2-7b-instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TRAC-MTRY/traclm-v2-7b-instruct-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="TRAC-MTRY/traclm-v2-7b-instruct-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TRAC-MTRY/traclm-v2-7b-instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("TRAC-MTRY/traclm-v2-7b-instruct-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 TRAC-MTRY/traclm-v2-7b-instruct-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TRAC-MTRY/traclm-v2-7b-instruct-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": "TRAC-MTRY/traclm-v2-7b-instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TRAC-MTRY/traclm-v2-7b-instruct-AWQ

SGLang

How to use TRAC-MTRY/traclm-v2-7b-instruct-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 "TRAC-MTRY/traclm-v2-7b-instruct-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": "TRAC-MTRY/traclm-v2-7b-instruct-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 "TRAC-MTRY/traclm-v2-7b-instruct-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": "TRAC-MTRY/traclm-v2-7b-instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TRAC-MTRY/traclm-v2-7b-instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/TRAC-MTRY/traclm-v2-7b-instruct-AWQ
```

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Model Card for traclm-v2-7b-instruct-GPTQ

This repo contains an AWQ quantization of TRAC-MTRY/traclm-v2-7b-instruct for utilization of the model on low-resource hardware.

Prompt Format

This model was fine-tuned with the alpaca prompt format. It is highly recommended that you use the same format for any interactions with the model. Failure to do so will degrade performance significantly.

Standard Alpaca Format:

### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n### Response:\n "

Input Field Variant:

### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n###Input:\n{input}\n\n### Response:\n "

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Dataset used to train TRAC-MTRY/traclm-v2-7b-instruct-AWQ

Paper for TRAC-MTRY/traclm-v2-7b-instruct-AWQ

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 13