Instructions to use TRAC-MTRY/traclm-v1-3b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TRAC-MTRY/traclm-v1-3b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="TRAC-MTRY/traclm-v1-3b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TRAC-MTRY/traclm-v1-3b-instruct")
model = AutoModelForCausalLM.from_pretrained("TRAC-MTRY/traclm-v1-3b-instruct")
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-v1-3b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TRAC-MTRY/traclm-v1-3b-instruct"
# 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-v1-3b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TRAC-MTRY/traclm-v1-3b-instruct

SGLang

How to use TRAC-MTRY/traclm-v1-3b-instruct 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-v1-3b-instruct" \
    --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-v1-3b-instruct",
		"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-v1-3b-instruct" \
        --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-v1-3b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TRAC-MTRY/traclm-v1-3b-instruct with Docker Model Runner:
```
docker model run hf.co/TRAC-MTRY/traclm-v1-3b-instruct
```

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

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 4
eval_batch_size: 4
seed: 8
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
11.2812	0.0	1	11.5156
5.0938	0.2	62	5.1016
3.5703	0.4	124	3.7161
2.582	0.6	186	2.9010
2.2109	0.8	248	2.5156
1.9824	1.0	310	2.3477
1.8594	1.18	372	2.1960
1.748	1.38	434	2.1667
1.748	1.58	496	2.0195
1.7617	1.78	558	2.0749
1.6582	1.98	620	1.9095
1.5762	2.16	682	1.9036
1.5586	2.36	744	1.8457
1.6016	2.56	806	1.8112
1.5195	2.76	868	1.8034
1.5645	2.96	930	1.7773
1.457	3.14	992	1.7474
1.4883	3.34	1054	1.7467
1.4648	3.54	1116	1.7676
1.5195	3.74	1178	1.7383
1.4531	3.94	1240	1.7383
1.4648	4.12	1302	1.7181
1.4121	4.32	1364	1.7272
1.4727	4.52	1426	1.7259
1.4219	4.72	1488	1.7240
1.5137	4.92	1550	1.7227

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0

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Safetensors

Model size

3B params

Tensor type

BF16

TRAC-MTRY
/

traclm-v1-3b-instruct

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train TRAC-MTRY/traclm-v1-3b-instruct