Instructions to use flytech/togetherchat-dev-7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flytech/togetherchat-dev-7b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="flytech/togetherchat-dev-7b-v2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flytech/togetherchat-dev-7b-v2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("flytech/togetherchat-dev-7b-v2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use flytech/togetherchat-dev-7b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flytech/togetherchat-dev-7b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flytech/togetherchat-dev-7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/flytech/togetherchat-dev-7b-v2

SGLang

How to use flytech/togetherchat-dev-7b-v2 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 "flytech/togetherchat-dev-7b-v2" \
    --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": "flytech/togetherchat-dev-7b-v2",
		"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 "flytech/togetherchat-dev-7b-v2" \
        --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": "flytech/togetherchat-dev-7b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flytech/togetherchat-dev-7b-v2 with Docker Model Runner:
```
docker model run hf.co/flytech/togetherchat-dev-7b-v2
```

togetherchat-dev-7b-v2

This model is a fine-tuned version of togethercomputer/LLaMA-2-7B-32K on 25000 entries for 3 epochs.

Model description

Model can be used for text-to-code generation and for further fine-tuning, Colab notebook example (on free T4 GPU) soon!

Datasets used:

evol-codealpaca-80k - 10000 entries
codealpaca-20k - 10000 entries
open-platypus - 5000 entries

Intended uses & limitations

Please remember that model may (and will) produce inaccurate informations, you need to fine-tune it for your specific task.

Training and evaluation data

See 'Metrics'

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 10
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Downloads last month: 8

Model tree for flytech/togetherchat-dev-7b-v2

Base model

togethercomputer/LLaMA-2-7B-32K

Finetuned

(4)

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