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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

flytech commited on Sep 9, 2023

Commit

4d8b599

1 Parent(s): 725edc8

Update README.md

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README.md +15 -15

README.md CHANGED Viewed

@@ -20,21 +20,21 @@ evol_codealpaca_dataset = load_dataset("theblackcat102/evol-codealpaca-v1")
 ## Model description
 Step	Training Loss
--------------
--60	    1.293000-
--120	0.673600-
--180	0.633200-
--240	0.611600-
--300	0.633000-
--360	0.589500-
--480	0.587600-
--540	0.569000-
--600	0.548700-
--660	0.553100-
--720	0.531500-
--780	0.506400-
--840	0.512500-
--------------
 ## Intended uses & limitations

 ## Model description
 Step	Training Loss
+---------------------
+    60	1.293000
+    120	0.673600
+    180	0.633200
+    240	0.611600
+    300	0.633000
+    360	0.589500
+    480	0.587600
+    540	0.569000
+    600	0.548700
+    660	0.553100
+    720	0.531500
+    780	0.506400
+    840	0.512500
 ## Intended uses & limitations