Instructions to use 080-ai/flintlock_3B_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 080-ai/flintlock_3B_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="080-ai/flintlock_3B_v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("080-ai/flintlock_3B_v0.1")
model = AutoModelForCausalLM.from_pretrained("080-ai/flintlock_3B_v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 080-ai/flintlock_3B_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "080-ai/flintlock_3B_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "080-ai/flintlock_3B_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/080-ai/flintlock_3B_v0.1

SGLang

How to use 080-ai/flintlock_3B_v0.1 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 "080-ai/flintlock_3B_v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "080-ai/flintlock_3B_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "080-ai/flintlock_3B_v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "080-ai/flintlock_3B_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 080-ai/flintlock_3B_v0.1 with Docker Model Runner:
```
docker model run hf.co/080-ai/flintlock_3B_v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model

A fine-tuned openllama 3B model, using primary sources from US History to provide a deeper understanding of the historical context.

Run history:

train/epoch ▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇███
train/global_step ▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇███
train/grad_norm ██▄▅▄▅▃▂▂▄▂▂▂▂▁▁▁▁▁▁
train/learning_rate ▂▂▃▄▅▅▆▇▇█▇▇▆▅▅▄▃▂▂▁
train/loss ▇█▇▆▅▅▄▄▃▃▂▂▂▁▂▁▁▁▁▁
train/total_flos ▁
train/train_loss ▁
train/train_runtime ▁
train/train_samples_per_second ▁
train/train_steps_per_second ▁

Run summary:

train/epoch 2.0
train/global_step 20
train/grad_norm 0.13779
train/learning_rate 0.0
train/loss 1.1365
train/total_flos 4.579249185376512e+16
train/train_loss 1.29891
train/train_runtime 1552.5749
train/train_samples_per_second 1.649
train/train_steps_per_second 0.013

Downloads last month: 1

Safetensors

Model size

3B params

Tensor type

F16

Model tree for 080-ai/flintlock_3B_v0.1

Quantizations

1 model

080-ai
/

flintlock_3B_v0.1

Model

Model tree for 080-ai/flintlock_3B_v0.1

Dataset used to train 080-ai/flintlock_3B_v0.1