Edit model card

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

πŸ’» 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

Downloads last month
7
Safetensors
Model size
8.03B params
Tensor type
BF16
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for AdamLucek/llama3-8b-code-sql-slerp

Collection including AdamLucek/llama3-8b-code-sql-slerp