Instructions to use latimar/Phind-Codellama-34B-v2-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use latimar/Phind-Codellama-34B-v2-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="latimar/Phind-Codellama-34B-v2-exl2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("latimar/Phind-Codellama-34B-v2-exl2")
model = AutoModelForCausalLM.from_pretrained("latimar/Phind-Codellama-34B-v2-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use latimar/Phind-Codellama-34B-v2-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "latimar/Phind-Codellama-34B-v2-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "latimar/Phind-Codellama-34B-v2-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/latimar/Phind-Codellama-34B-v2-exl2

SGLang

How to use latimar/Phind-Codellama-34B-v2-exl2 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 "latimar/Phind-Codellama-34B-v2-exl2" \
    --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": "latimar/Phind-Codellama-34B-v2-exl2",
		"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 "latimar/Phind-Codellama-34B-v2-exl2" \
        --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": "latimar/Phind-Codellama-34B-v2-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use latimar/Phind-Codellama-34B-v2-exl2 with Docker Model Runner:
```
docker model run hf.co/latimar/Phind-Codellama-34B-v2-exl2
```

latimar commited on Sep 15, 2023

Commit

e793e71

verified ·

1 Parent(s): 1bf4a80

Update README

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README.md +2 -0

README.md CHANGED Viewed

@@ -26,6 +26,7 @@ There are the following branches:
 5_0-bpw-h8
 4_625-bpw-h6
 4_125-bpw-h6
 2_75-bpw-h6
 2_55-bpw-h6
 ```
@@ -39,6 +40,7 @@ There are the following branches:
 | ----------- | ----------- | -------------- |
 |  2.55-h6    |  15.0901    |    10.56       |
 |  2.75-h6    |  13.6153    |    11.33       |
 |  4.125-h6   |  6.8095     |    16.65       |
 |  4.625-h6   |  6.7992     |    18.58       |
 |  5.0-h8     |  6.7785     |    20.09       |

 5_0-bpw-h8
 4_625-bpw-h6
 4_125-bpw-h6
+3_8-bpw-h6
 2_75-bpw-h6
 2_55-bpw-h6
 ```
 | ----------- | ----------- | -------------- |
 |  2.55-h6    |  15.0901    |    10.56       |
 |  2.75-h6    |  13.6153    |    11.33       |
+|  3.8-h6     |  6.8803     |    15.37       |
 |  4.125-h6   |  6.8095     |    16.65       |
 |  4.625-h6   |  6.7992     |    18.58       |
 |  5.0-h8     |  6.7785     |    20.09       |