Instructions to use forestai/fireact_llama_2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use forestai/fireact_llama_2_7b with Transformers:

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

pipe = pipeline("text-generation", model="forestai/fireact_llama_2_7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("forestai/fireact_llama_2_7b")
model = AutoModelForCausalLM.from_pretrained("forestai/fireact_llama_2_7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use forestai/fireact_llama_2_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "forestai/fireact_llama_2_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "forestai/fireact_llama_2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/forestai/fireact_llama_2_7b

SGLang

How to use forestai/fireact_llama_2_7b 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 "forestai/fireact_llama_2_7b" \
    --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": "forestai/fireact_llama_2_7b",
		"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 "forestai/fireact_llama_2_7b" \
        --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": "forestai/fireact_llama_2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use forestai/fireact_llama_2_7b with Docker Model Runner:
```
docker model run hf.co/forestai/fireact_llama_2_7b
```

Website: FireAct Agent

FireAct Llama-2/CodeLlama

FireAct Llama/CodeLlama is a collection of fine-tuned generative text models for performing ReAct with external search tools. Links to other models can be found in the Index section.

Foundation Model Details

Note: As the foundation models, Llama-2 and CodeLlama, are developed by Meta, please also read the guidance and license on their website, Llama-2 and CodeLlama, before using FireAct models.

Model Developers System 2 Research, Cambridge LTL, Monash University, Princeton PLI.

Variations FireAct models including Llama-2-7B full fine-tuned models, and Llama-2-[7B,13B], CodeLlama-[7B,13B,34B] LoRA fine-tuned models. All released models are fine-tuned on multi-task (HotpotQA/StrategyQA/MMLU) and multi-type (ReAct/CoT/Reflexion) data.

Input Models input text only.

Output Models generate text only.

Index

Full Fine-tuned Model

FireAct Llama-2:

fireact_llama_2_7b

LoRA Fine-tuned Model

FireAct Llama-2:

FireAct CodeLlama:

Downloads last month: 9

forestai
/

fireact_llama_2_7b

FireAct Llama-2/CodeLlama

Foundation Model Details

Index

Space using forestai/fireact_llama_2_7b 1