Instructions to use mrm8488/llama-2-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/llama-2-coder-7b with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/llama-2-coder-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/llama-2-coder-7b")
model = AutoModelForCausalLM.from_pretrained("mrm8488/llama-2-coder-7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrm8488/llama-2-coder-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/llama-2-coder-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/llama-2-coder-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/llama-2-coder-7b

SGLang

How to use mrm8488/llama-2-coder-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 "mrm8488/llama-2-coder-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": "mrm8488/llama-2-coder-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 "mrm8488/llama-2-coder-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": "mrm8488/llama-2-coder-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/llama-2-coder-7b with Docker Model Runner:
```
docker model run hf.co/mrm8488/llama-2-coder-7b
```

mrm8488 commited on Jul 26, 2023

Commit

7ef22d4

1 Parent(s): 73180e6

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -6

README.md CHANGED Viewed

@@ -3,6 +3,7 @@ tags:
 - generated_from_trainer
 - code
 - coding
 model-index:
 - name: FalCoder
   results: []
@@ -27,7 +28,7 @@ should probably proofread and complete it, then remove this comment. -->
 ## Model description 🧠
-[Llama-2](https://huggingface.co/tiiuae/falcon-7b)
 ## Training and evaluation data 📚
@@ -62,6 +63,11 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
 def generate(
         instruction,
         max_new_tokens=128,
@@ -69,9 +75,9 @@ def generate(
         top_p=0.75,
         top_k=40,
         num_beams=4,
-        **kwargs
 ):
-    prompt = instruction + "\n### Solution:\n"
     print(prompt)
     inputs = tokenizer(prompt, return_tensors="pt")
     input_ids = inputs["input_ids"].to("cuda")
@@ -97,9 +103,14 @@ def generate(
     output = tokenizer.decode(s)
     return output.split("### Solution:")[1].lstrip("\n")
-instruction = "Design a class for representing a person in Python."
 print(generate(instruction))
 ```
-### Citation

 - generated_from_trainer
 - code
 - coding
+- llama
 model-index:
 - name: FalCoder
   results: []
 ## Model description 🧠
+[Llama-2](https://huggingface.co/meta-llama/Llama-2-7b)
 ## Training and evaluation data 📚
 model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
+def create_prompt(instruction):
+  system = "You are a coding assistant that will help the user to resolve the following instruction:"
+  instruction = "### Instruction: " + instruction
+  return system + "\n" + instruction + "\n\n" + "### Solution:" + "\n"
 def generate(
         instruction,
         max_new_tokens=128,
         top_p=0.75,
         top_k=40,
         num_beams=4,
+        **kwargs,
 ):
+    prompt = create_prompt(instruction)
     print(prompt)
     inputs = tokenizer(prompt, return_tensors="pt")
     input_ids = inputs["input_ids"].to("cuda")
     output = tokenizer.decode(s)
     return output.split("### Solution:")[1].lstrip("\n")
+instruction = """
+Edit the following XML code to add a navigation bar to the top of a web page
+<html>
+<head>
+  <title>CliBrAIn</title>
+</head>
+"""
 print(generate(instruction))
 ```
+### Citation