Instructions to use TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ

SGLang

How to use TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ 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 "TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ" \
    --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": "TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ",
		"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 "TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ" \
        --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": "TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ
```

TheBloke commited on Sep 27, 2023

Commit

5d65e43

1 Parent(s): 2649268

Update base_model formatting

Browse files

Files changed (1) hide show

README.md +15 -16

README.md CHANGED Viewed

@@ -1,29 +1,28 @@
 ---
-base_model: https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1
-inference: false
 license: llama2
 model-index:
 - name: Phind-CodeLlama-34B-v1
   results:
-  - dataset:
       name: HumanEval
       type: openai_humaneval
     metrics:
-    - name: pass@1
-      type: pass@1
       value: 69.5%
       verified: false
-    task:
-      type: text-generation
-model_creator: Phind
-model_name: Phind CodeLlama 34B Python v1
-model_type: llama
-prompt_template: '{prompt} \n
-  '
-quantized_by: TheBloke
-tags:
-- code llama
 ---
 <!-- header start -->

 ---
 license: llama2
+tags:
+- code llama
+base_model: Phind/Phind-CodeLlama-34B-Python-v1
+inference: false
+model_creator: Phind
+model_type: llama
+prompt_template: '{prompt} \n
+  '
+quantized_by: TheBloke
 model-index:
 - name: Phind-CodeLlama-34B-v1
   results:
+  - task:
+      type: text-generation
+    dataset:
       name: HumanEval
       type: openai_humaneval
     metrics:
+    - type: pass@1
       value: 69.5%
+      name: pass@1
       verified: false
 ---
 <!-- header start -->