Instructions to use AdamLucek/llama3-8b-code-sql-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdamLucek/llama3-8b-code-sql-slerp with Transformers:

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

pipe = pipeline("text-generation", model="AdamLucek/llama3-8b-code-sql-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdamLucek/llama3-8b-code-sql-slerp")
model = AutoModelForCausalLM.from_pretrained("AdamLucek/llama3-8b-code-sql-slerp", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdamLucek/llama3-8b-code-sql-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdamLucek/llama3-8b-code-sql-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/llama3-8b-code-sql-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdamLucek/llama3-8b-code-sql-slerp

SGLang

How to use AdamLucek/llama3-8b-code-sql-slerp 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 "AdamLucek/llama3-8b-code-sql-slerp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/llama3-8b-code-sql-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "AdamLucek/llama3-8b-code-sql-slerp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdamLucek/llama3-8b-code-sql-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdamLucek/llama3-8b-code-sql-slerp with Docker Model Runner:
```
docker model run hf.co/AdamLucek/llama3-8b-code-sql-slerp
```

llama3-8b-code-sql-slerp / README.md

AdamLucek

Update README.md

cbd50d3 verified about 2 years ago

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metadata

base_model:
  - ajibawa-2023/Code-Llama-3-8B
  - defog/llama-3-sqlcoder-8b
library_name: transformers
tags:
  - mergekit
  - merge

llama3-8b-code-sql-slerp

llama3-8b-code-sql-slerp is a merge of two fine tuned Llama 3 8B models for coding, intended to have a solid programming foundation with an expertise in SQL.

🤏 Models Merged

Merge of pre-trained language models merged using the SLERP merge method with mergekit.

The following models were included in the merge:

🧩 Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ajibawa-2023/Code-Llama-3-8B
        layer_range: [0, 32]
      - model: defog/llama-3-sqlcoder-8b
        layer_range: [0, 32]
merge_method: slerp
base_model: ajibawa-2023/Code-Llama-3-8B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.3, 0.5, 0.7, 0.5]
    - filter: mlp
      value: [0, 0.3, 0.5, 0.7, 0.5]
    - value: 0.4 # fallback for rest of tensors
dtype: bfloat16

💻 Usage

Loading in 8-bit Quantization

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

tokenizer = AutoTokenizer.from_pretrained("AdamLucek/llama3-8b-code-sql-slerp")
model = AutoModelForCausalLM.from_pretrained(
    "AdamLucek/llama3-8b-code-sql-slerp",
    device_map="cuda",
    quantization_config=BitsAndBytesConfig(load_in_8bit=True)
)

# Prepare the input text
input_text = "Can you write a query to retrieve the names and email addresses of all customers who have made purchases totaling over $1000 in the last month from our 'sales' database?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate the output
outputs = model.generate(
    **input_ids,
    max_new_tokens=256,
    pad_token_id=tokenizer.eos_token_id
)

# Decode and print the generated text
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Output

\```sql
SELECT c.name, c.email
FROM customers c
JOIN sales s ON c.customer_id = s.customer_id
WHERE s.purchase_date >= DATE_SUB(CURRENT_DATE, INTERVAL 1 MONTH)
GROUP BY c.name, c.email
HAVING SUM(s.amount) > 1000;
\```

This query joins the 'customers' and'sales' tables on the 'customer_id' field, filters for sales made in the last month, groups the results by customer name and email, and then applies a condition to only include customers whose total purchase amount exceeds $1000. The result will be a list of names and email addresses for customers who have made purchases totaling over $1000 in the last month.

backslash added for formatting