Instructions to use ajay141/chat-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajay141/chat-sql with Transformers:

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

pipe = pipeline("text-generation", model="ajay141/chat-sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("ajay141/chat-sql")
model = AutoModelForCausalLM.from_pretrained("ajay141/chat-sql", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ajay141/chat-sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajay141/chat-sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajay141/chat-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ajay141/chat-sql

SGLang

How to use ajay141/chat-sql 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 "ajay141/chat-sql" \
    --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": "ajay141/chat-sql",
		"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 "ajay141/chat-sql" \
        --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": "ajay141/chat-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ajay141/chat-sql with Docker Model Runner:
```
docker model run hf.co/ajay141/chat-sql
```

chat-sql / README.md

ajay141

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---
tags:
- merge
- mergekit
- lazymergekit
- yuiseki/tinyllama-coder-sql-en-v0.1
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
base_model:
- yuiseki/tinyllama-coder-sql-en-v0.1
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# chat-sql

chat-sql is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yuiseki/tinyllama-coder-sql-en-v0.1](https://huggingface.co/yuiseki/tinyllama-coder-sql-en-v0.1)
* [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: yuiseki/tinyllama-coder-sql-en-v0.1
        layer_range: [0, 22]
      - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
        layer_range: [0, 22]
merge_method: slerp
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
parameters:
  t:
    - filter: lm_head
      value: [0.75]
    - filter: embed_tokens
      value: [0.75]
    - filter: self_attn
      value: [0.75,0.25]
    - filter: mlp
      value: [0.25,0.75]
    - filter: layernorm
      value: [0.5,0.5]
    - filter: modelnorm
      value: [0.75]
    - value: 0.5
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ajay141/chat-sql"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

	---
	tags:
	- merge
	- mergekit
	- lazymergekit
	- yuiseki/tinyllama-coder-sql-en-v0.1
	- TinyLlama/TinyLlama-1.1B-Chat-v1.0
	base_model:
	- yuiseki/tinyllama-coder-sql-en-v0.1
	- TinyLlama/TinyLlama-1.1B-Chat-v1.0
	---

	# chat-sql

	chat-sql is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [yuiseki/tinyllama-coder-sql-en-v0.1](https://huggingface.co/yuiseki/tinyllama-coder-sql-en-v0.1)
	* [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)

	## 🧩 Configuration

	```yaml
	slices:
	- sources:
	- model: yuiseki/tinyllama-coder-sql-en-v0.1
	layer_range: [0, 22]
	- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	layer_range: [0, 22]
	merge_method: slerp
	base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	parameters:
	t:
	- filter: lm_head
	value: [0.75]
	- filter: embed_tokens
	value: [0.75]
	- filter: self_attn
	value: [0.75,0.25]
	- filter: mlp
	value: [0.25,0.75]
	- filter: layernorm
	value: [0.5,0.5]
	- filter: modelnorm
	value: [0.75]
	- value: 0.5
	dtype: bfloat16
	```

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "ajay141/chat-sql"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```